Group6_lab3_python__.ipynb

{
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  {
   "cell_type": "markdown",
   "id": "0230f06d",
   "metadata": {},
   "source": [
    "# Replication of Lab 3: \"Analyzing RCT data with Precision Adjustment\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d01e083",
   "metadata": {},
   "source": [
    "# Group 6"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "04d3e9f8",
   "metadata": {},
   "source": [
    "Members:\n",
    "1. Eljaer Eusebio\n",
    "2. Claudia Vivas\n",
    "3. Luis Sandoval\n",
    "4. Andre Tapia"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c842df7a",
   "metadata": {},
   "source": [
    "# QUESTION 1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9d4825c",
   "metadata": {},
   "source": [
    "### MULTICOLINEARITY\n",
    "\n",
    "We talk about multicollinearity when independent variables of some linear regresion have a high correlation between each one. The results could be biased to the analysis, so the model has a problem. Two cases arise form multicollinearity: Perfect Multicolinearity and Imperfect Multicollinearity. In the first case, it happens when two or more regressors X have a linear and exact relationship between each one , which breaks one of the linear regresion's assumption.\n",
    "\n",
    "For example: \n",
    "We relate food expenditure to family income, which is labor income and labor not-labor income. First one is related with labor supply and salary. In the other case, it is related with income not neccesarily to family (banks transferes, rental income, pensions). Therefore, earned income and unearned income are expected to affect spending. But, if we add total income on the linear regression:\n",
    "\n",
    "$$\n",
    "Galim_i = \\beta_1 + \\beta_2 LaborIncome_i + \\beta_3 NotLaborIncome_i + \\beta_4 TotalIncome_i + \\varepsilon_i\n",
    "$$\n",
    "\n",
    "We Know \n",
    "\n",
    "$$\n",
    " LaborIncome_i + NotLaborIncome_i = TotalIncome_i \n",
    "$$\n",
    "\n",
    "In this case the number of regressors(K) es equal 4, but the range ($X$) is 3. So, the order of matrix $XX'$ is 4x4 but its range in low. In this case, We can't get ${\\beta}^~$ because doesn't exist ${XX'}^-1$.\n",
    "\n",
    "The problem is that \"Total income\" did not give us more information than \"Earnings from work\" and \"Income from non-work.\" It is redundant information, so it is necessary to delete it.\n",
    "\n",
    "\n",
    "About Imperfect Multicollinearity, the relantionship between explanatory variables is strong but not perfectly linear like the first case.\n",
    "\n",
    "For example:\n",
    "We wish to explain the relantionship between consume and incomes plus wealth. Following the next model\n",
    "\n",
    "$$\n",
    " Consume_i = \\beta_1 + \\beta_2 Income_i + \\beta_3 Wealth_i + \\varepsilon_i\n",
    "$$\n",
    "\n",
    "The probability of having high correlation between \"Income\" and \"Wealth\" exist. If we focus on high-income households, it is very likely that they have significant accumulated wealth. But the relationship is not perfect because it is possible for someone to have low income but high wealth, and vice versa."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "6d2bb51d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "X = np.random.normal(0, 1, (9, 10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "028545f7",
   "metadata": {},
   "outputs": [
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       "array([[ 1.20319755, -0.31448101,  1.07565378,  0.22233824,  1.73073296,\n",
       "         2.23259093, -0.78140271, -1.12781764, -0.83786062,  1.12452541],\n",
       "       [ 0.20131441,  0.33152982,  0.67987766, -0.82458555, -0.10760191,\n",
       "        -0.91986818, -0.66915809,  0.64121504, -1.17982226,  0.64795184],\n",
       "       [-0.01808185, -1.77952696, -2.16623827,  0.1202153 ,  0.40614339,\n",
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       "         0.65570808,  0.05560016,  0.29088831,  1.35935256,  0.26576211],\n",
       "       [ 1.05021326,  0.27993574,  0.80775039,  0.54501665,  1.78349067,\n",
       "         1.80815599, -0.05037539,  0.41148237,  1.92820848, -0.24692452]])"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "bdf82b81",
   "metadata": {},
   "outputs": [],
   "source": [
    "Y = X[6,:]*X[7,:]*X[8,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "bc17dde2",
   "metadata": {},
   "outputs": [],
   "source": [
    "Z = np.vstack((X,Y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "839c82dd",
   "metadata": {},
   "outputs": [],
   "source": [
    "Z = np.asmatrix(Z)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "43aa6cd1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10, 10)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.shape(Z)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "572a963b",
   "metadata": {},
   "outputs": [
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       "matrix([[ 2.78079650e-02,  1.35762960e+00,  3.43202599e-01,\n",
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       "          1.64470473e+00],\n",
       "        [-1.79229403e-01,  1.57747449e+01,  3.98908184e+00,\n",
       "          1.45025226e+00,  9.04335833e+00, -8.92837649e-01,\n",
       "          8.71326281e+00,  2.81711432e+00, -1.06345472e-01,\n",
       "         -3.08130017e+00],\n",
       "        [-1.87241859e-01,  2.72416704e+00,  7.09509164e-01,\n",
       "          4.37527535e-01,  1.21106769e+00, -2.63759783e-01,\n",
       "          1.26058091e+00,  3.48377311e-01,  2.55608594e-01,\n",
       "         -4.06451897e-01],\n",
       "        [ 1.96722704e-02, -4.45754864e+00, -1.11961197e+00,\n",
       "         -4.44935308e-01, -2.35032964e+00,  1.41179762e-01,\n",
       "         -2.34546596e+00, -6.31410575e-01,  1.37844606e-01,\n",
       "          7.09851401e-01],\n",
       "        [ 3.36937386e-01,  4.81219680e-02,  1.98701773e-02,\n",
       "         -8.33019107e-03, -1.99245513e-01, -3.46244223e-01,\n",
       "          7.57686645e-02,  2.09602894e-01, -1.15807086e-01,\n",
       "         -2.02747578e-01]])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.linalg.inv(Z)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b28f98b5",
   "metadata": {},
   "source": [
    "According to the documentation of Numpy package, to calculate the generalized inverse of a matrix it is used singular-value decomposition (SVD) method . This optimization method allows inverting no-singular matrixes. In the optimization method lies the difference in the results of this question"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d1142f70",
   "metadata": {},
   "source": [
    "# QUESTION 2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "997bff7f",
   "metadata": {},
   "source": [
    "##### 1.1. Focus on the Treatment group 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "44b9af07",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import pyreadr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e453b129",
   "metadata": {},
   "outputs": [],
   "source": [
    "## loading the data\n",
    "Penn = pd.read_csv(\"../data/penn_jae.dat\" , sep='\\s', engine='python')\n",
    "n = Penn.shape[0]\n",
    "p_1 = Penn.shape[1]\n",
    "Penn = Penn[ (Penn['tg'] == 2) | (Penn['tg'] == 0) ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "f2bace1e",
   "metadata": {},
   "outputs": [
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13911</th>\n",
       "      <td>10691</td>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5782 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        abdt  tg  inuidur1  inuidur2  female  black  hispanic  othrace  dep  \\\n",
       "0      10824   0        18        18       0      0         0        0    2   \n",
       "1      10635   2         7         3       0      0         0        0    0   \n",
       "3      10824   0         1         1       0      0         0        0    0   \n",
       "4      10747   0        27        27       0      0         0        0    0   \n",
       "9      10754   2        20        20       1      0         0        0    0   \n",
       "...      ...  ..       ...       ...     ...    ...       ...      ...  ...   \n",
       "13900  10845   0         6         6       1      0         0        1    2   \n",
       "13901  10705   2        14        14       0      1         0        0    0   \n",
       "13906  10558   0         9         9       0      0         0        0    2   \n",
       "13909  10677   2         4         4       1      0         0        0    0   \n",
       "13911  10691   0        27        27       0      0         0        0    0   \n",
       "\n",
       "       q1  ...  q5  q6  recall  agelt35  agegt54  durable  nondurable  lusd  \\\n",
       "0       0  ...   0   0       0        0        0        0           0     1   \n",
       "1       0  ...   0   0       1        0        0        0           1     0   \n",
       "3       0  ...   0   0       0        0        0        0           1     0   \n",
       "4       0  ...   0   0       0        0        0        0           1     0   \n",
       "9       0  ...   0   0       0        0        0        0           0     0   \n",
       "...    ..  ...  ..  ..     ...      ...      ...      ...         ...   ...   \n",
       "13900   0  ...   0   0       0        0        0        1           0     0   \n",
       "13901   0  ...   0   0       0        0        1        0           0     0   \n",
       "13906   0  ...   0   0       1        0        0        0           1     0   \n",
       "13909   0  ...   0   0       0        1        0        0           1     0   \n",
       "13911   0  ...   0   0       0        1        1        0           1     0   \n",
       "\n",
       "       husd  muld  \n",
       "0         0   NaN  \n",
       "1         0   NaN  \n",
       "3         0   NaN  \n",
       "4         0   NaN  \n",
       "9         1   NaN  \n",
       "...     ...   ...  \n",
       "13900     1   NaN  \n",
       "13901     1   NaN  \n",
       "13906     0   NaN  \n",
       "13909     0   NaN  \n",
       "13911     0   NaN  \n",
       "\n",
       "[5782 rows x 24 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Penn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "0219a78d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>13909</th>\n",
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       "      <th>13911</th>\n",
       "      <td>0</td>\n",
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       "  </tbody>\n",
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       "<p>5782 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       tg  inuidur1\n",
       "0       0        18\n",
       "1       2         7\n",
       "3       0         1\n",
       "4       0        27\n",
       "9       2        20\n",
       "...    ..       ...\n",
       "13900   0         6\n",
       "13901   2        14\n",
       "13906   0         9\n",
       "13909   2         4\n",
       "13911   0        27\n",
       "\n",
       "[5782 rows x 2 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Penn_1 =Penn[[\"tg\",\"inuidur1\"]]\n",
    "Penn_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "97ae15b4",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>inuidur1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <th>1</th>\n",
       "      <td>7</td>\n",
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       "<p>2428 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       inuidur1\n",
       "1             7\n",
       "9            20\n",
       "21           27\n",
       "23            3\n",
       "27           17\n",
       "...         ...\n",
       "13869        12\n",
       "13878         3\n",
       "13881         7\n",
       "13901        14\n",
       "13909         4\n",
       "\n",
       "[2428 rows x 1 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "treatment = Penn_1[Penn_1[\"tg\"] == 2]\n",
    "treatment=treatment[[\"inuidur1\"]]\n",
    "treatment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "9713ab48",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
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       "      <th>13911</th>\n",
       "      <td>27</td>\n",
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       "<p>3354 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       inuidur1\n",
       "0            18\n",
       "3             1\n",
       "4            27\n",
       "12           27\n",
       "13           27\n",
       "...         ...\n",
       "13895         1\n",
       "13898        23\n",
       "13900         6\n",
       "13906         9\n",
       "13911        27\n",
       "\n",
       "[3354 rows x 1 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "control = Penn_1[Penn_1[\"tg\"] == 0]\n",
    "control = control[[\"inuidur1\"]]\n",
    "control"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "80f80d47",
   "metadata": {},
   "source": [
    "##### 1.2. Plot two histograms for treatment and control group to see the distribution of the outcome variable inuidur1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "c56aff87",
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "import numpy as np #Libreria para vectores\n",
    "import matplotlib.pyplot as plt #Libreria para graficos\n",
    "import os #Libreria para manejo del SO\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "60f72622",
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.set()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "286af8f5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams[\"figure.figsize\"] = (8,4)\n",
    "plt.hist(x = treatment,bins=20,density=True)\n",
    "plt.title(\"Histogram of percent of people's treatment and how many weeks they took to find a job\")\n",
    "plt.xlabel(\"How many weeks they took to find a job(inuidur1)\")\n",
    "plt.ylabel(\"Density\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b08c4e9a",
   "metadata": {},
   "source": [
    "This graphic show us that there were more than 11% of treatment's people whom took only one week to find a job.\n",
    "Adittionally, there were more than 8% of treatment's people to whom took between 26-28 weeks to find a job.\n",
    "Therefore, there were very few people (less than 1%) who took between 30 and 40 weeks to find a job.\n",
    "Finally, everyone else took between 5 and 25 weeks to find a job."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "890bdc3b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams[\"figure.figsize\"] = (8,4)\n",
    "plt.hist(x = control,bins=20,density=True)\n",
    "plt.title(\"Histogram of percent of people's control and how many weeks they took to find a job\")\n",
    "plt.xlabel(\"How many weeks they took to find a job(inuidur1)\")\n",
    "plt.ylabel(\"Density\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed1607af",
   "metadata": {},
   "source": [
    "This graphic show us that there were more than $10\\%$  of control's people whom took only one week to find a job.Adittionally, there were almost $8\\%$ of treatment's people to whom took between 26-28 weeks to find a job.Therefore, there were very few people (less than  1% ) who took between 30 and 40 weeks to find a job. Finally, everyone else took between 5 and 25 weeks to find a job."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5e0ce73",
   "metadata": {},
   "source": [
    "The distribution of the $inuidur1$ variable is quite similar for the treatment and control groups.  This means that the treatment did not have a differential effect on unemployment, this could be explained by the external influence of the covariates.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "95d08f36",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 5782 entries, 0 to 13911\n",
      "Data columns (total 24 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   abdt         5782 non-null   int64  \n",
      " 1   tg           5782 non-null   int64  \n",
      " 2   inuidur1     5782 non-null   int64  \n",
      " 3   inuidur2     5782 non-null   int64  \n",
      " 4   female       5782 non-null   int64  \n",
      " 5   black        5782 non-null   int64  \n",
      " 6   hispanic     5782 non-null   int64  \n",
      " 7   othrace      5782 non-null   int64  \n",
      " 8   dep          5782 non-null   int64  \n",
      " 9   q1           5782 non-null   int64  \n",
      " 10  q2           5782 non-null   int64  \n",
      " 11  q3           5782 non-null   int64  \n",
      " 12  q4           5782 non-null   int64  \n",
      " 13  Unnamed: 13  5782 non-null   int64  \n",
      " 14  q5           5782 non-null   int64  \n",
      " 15  q6           5782 non-null   int64  \n",
      " 16  recall       5782 non-null   int64  \n",
      " 17  agelt35      5782 non-null   int64  \n",
      " 18  agegt54      5782 non-null   int64  \n",
      " 19  durable      5782 non-null   int64  \n",
      " 20  nondurable   5782 non-null   int64  \n",
      " 21  lusd         5782 non-null   int64  \n",
      " 22  husd         5782 non-null   int64  \n",
      " 23  muld         0 non-null      float64\n",
      "dtypes: float64(1), int64(23)\n",
      "memory usage: 1.1 MB\n"
     ]
    }
   ],
   "source": [
    "#this columns were not dropped out :  Unnamed: 13, recall\n",
    "Penn.columns\n",
    "Penn.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "a3a7c4d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>abdt</th>\n",
       "      <th>tg</th>\n",
       "      <th>inuidur1</th>\n",
       "      <th>inuidur2</th>\n",
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       "      <th>q1</th>\n",
       "      <th>...</th>\n",
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       "      <th>nondurable</th>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13900</th>\n",
       "      <td>10845</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13901</th>\n",
       "      <td>10705</td>\n",
       "      <td>2</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13906</th>\n",
       "      <td>10558</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13909</th>\n",
       "      <td>10677</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13911</th>\n",
       "      <td>10691</td>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5782 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        abdt  tg  inuidur1  inuidur2  female  black  hispanic  othrace dep  \\\n",
       "0      10824   0        18        18       0      0         0        0   2   \n",
       "1      10635   2         7         3       0      0         0        0   0   \n",
       "3      10824   0         1         1       0      0         0        0   0   \n",
       "4      10747   0        27        27       0      0         0        0   0   \n",
       "9      10754   2        20        20       1      0         0        0   0   \n",
       "...      ...  ..       ...       ...     ...    ...       ...      ...  ..   \n",
       "13900  10845   0         6         6       1      0         0        1   2   \n",
       "13901  10705   2        14        14       0      1         0        0   0   \n",
       "13906  10558   0         9         9       0      0         0        0   2   \n",
       "13909  10677   2         4         4       1      0         0        0   0   \n",
       "13911  10691   0        27        27       0      0         0        0   0   \n",
       "\n",
       "       q1  ...  q6  recall  agelt35  agegt54  durable  nondurable  lusd  husd  \\\n",
       "0       0  ...   0       0        0        0        0           0     1     0   \n",
       "1       0  ...   0       1        0        0        0           1     0     0   \n",
       "3       0  ...   0       0        0        0        0           1     0     0   \n",
       "4       0  ...   0       0        0        0        0           1     0     0   \n",
       "9       0  ...   0       0        0        0        0           0     0     1   \n",
       "...    ..  ...  ..     ...      ...      ...      ...         ...   ...   ...   \n",
       "13900   0  ...   0       0        0        0        1           0     0     1   \n",
       "13901   0  ...   0       0        0        1        0           0     0     1   \n",
       "13906   0  ...   0       1        0        0        0           1     0     0   \n",
       "13909   0  ...   0       0        1        0        0           1     0     0   \n",
       "13911   0  ...   0       0        1        1        0           1     0     0   \n",
       "\n",
       "       muld  T2  \n",
       "0       NaN   0  \n",
       "1       NaN   1  \n",
       "3       NaN   0  \n",
       "4       NaN   0  \n",
       "9       NaN   1  \n",
       "...     ...  ..  \n",
       "13900   NaN   0  \n",
       "13901   NaN   1  \n",
       "13906   NaN   0  \n",
       "13909   NaN   1  \n",
       "13911   NaN   0  \n",
       "\n",
       "[5782 rows x 25 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Dependent variable\n",
    "Penn['T2'] = (Penn[['tg']]==2).astype(int)\n",
    "\n",
    "# Create category variable\n",
    "Penn['dep'] = Penn['dep'].astype( 'category' )\n",
    "Penn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "40aea892",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[2, 0, 1]\n",
       "Categories (3, int64): [2, 0, 1]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Penn['dep'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1037b309",
   "metadata": {},
   "source": [
    "##### 1.3. Run all specifications:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b95a9163",
   "metadata": {},
   "source": [
    "###### 1.3.1. classical 2-sample approach, no adjustment (CL)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "09af6772",
   "metadata": {},
   "outputs": [],
   "source": [
    "import statsmodels.api as sm\n",
    "import statsmodels.formula.api as smf\n",
    "import patsy\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "4eaadfdd",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = Penn[['T2']].reset_index( drop = True )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "dbb1bb79",
   "metadata": {},
   "outputs": [],
   "source": [
    "result = pyreadr.read_r('../data/m_reg.RData')\n",
    "X_vars = result['X1']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "80cf8ebb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                     T2   R-squared:                       0.016\n",
      "Model:                            OLS   Adj. R-squared:                 -0.002\n",
      "Method:                 Least Squares   F-statistic:                     4.204\n",
      "Date:                Fri, 24 Sep 2021   Prob (F-statistic):           2.42e-39\n",
      "Time:                        17:40:20   Log-Likelihood:                -4075.6\n",
      "No. Observations:                5782   AIC:                             8357.\n",
      "Df Residuals:                    5679   BIC:                             9043.\n",
      "Df Model:                         102                                         \n",
      "Covariance Type:                  HC1                                         \n",
      "========================================================================================\n",
      "                           coef    std err          t      P>|t|      [0.025      0.975]\n",
      "----------------------------------------------------------------------------------------\n",
      "(Intercept)              0.5171      0.157      3.296      0.001       0.210       0.825\n",
      "female                   0.0056      0.132      0.042      0.966      -0.254       0.265\n",
      "black                   -0.0308      0.085     -0.363      0.716      -0.197       0.135\n",
      "othrace                 -0.4279      0.301     -1.424      0.155      -1.017       0.161\n",
      "factor(dep)1             0.1577      0.185      0.851      0.395      -0.205       0.521\n",
      "factor(dep)2            -0.1928      0.151     -1.278      0.201      -0.489       0.103\n",
      "q2                      -0.1092      0.158     -0.690      0.490      -0.420       0.201\n",
      "q3                      -0.1076      0.158     -0.681      0.496      -0.417       0.202\n",
      "q4                      -0.0621      0.158     -0.393      0.694      -0.372       0.248\n",
      "q5                      -0.0499      0.158     -0.317      0.752      -0.359       0.259\n",
      "q6                      -0.2176      0.156     -1.397      0.163      -0.523       0.088\n",
      "agelt35                 -0.0643      0.133     -0.485      0.628      -0.324       0.196\n",
      "agegt54                  0.0259      0.240      0.108      0.914      -0.444       0.496\n",
      "durable                 -0.1256      0.176     -0.712      0.476      -0.471       0.220\n",
      "lusd                     0.0861      0.069      1.246      0.213      -0.049       0.221\n",
      "husd                    -0.0182      0.076     -0.238      0.812      -0.167       0.131\n",
      "female:black             0.0027      0.044      0.061      0.951      -0.083       0.089\n",
      "female:othrace           0.1015      0.206      0.492      0.623      -0.303       0.506\n",
      "female:factor(dep)1     -0.0179      0.045     -0.398      0.691      -0.106       0.070\n",
      "female:factor(dep)2      0.0219      0.040      0.553      0.581      -0.056       0.100\n",
      "female:q2               -0.0995      0.132     -0.753      0.452      -0.359       0.160\n",
      "female:q3               -0.0144      0.132     -0.109      0.913      -0.273       0.244\n",
      "female:q4               -0.0167      0.132     -0.127      0.899      -0.275       0.241\n",
      "female:q5               -0.0586      0.131     -0.447      0.655      -0.316       0.198\n",
      "female:q6                0.0550      0.140      0.394      0.694      -0.219       0.329\n",
      "female:agelt35           0.0333      0.030      1.127      0.260      -0.025       0.091\n",
      "female:agegt54          -0.0110      0.050     -0.219      0.826      -0.109       0.087\n",
      "female:durable          -0.0586      0.044     -1.340      0.180      -0.144       0.027\n",
      "female:lusd              0.0051      0.033      0.153      0.878      -0.060       0.070\n",
      "female:husd              0.0187      0.037      0.509      0.611      -0.053       0.091\n",
      "black:factor(dep)1      -0.0139      0.068     -0.206      0.837      -0.146       0.118\n",
      "black:factor(dep)2      -0.0230      0.062     -0.370      0.712      -0.145       0.099\n",
      "black:q2                 0.1311      0.091      1.440      0.150      -0.047       0.309\n",
      "black:q3                 0.0357      0.088      0.407      0.684      -0.136       0.207\n",
      "black:q4                -0.1095      0.089     -1.227      0.220      -0.284       0.065\n",
      "black:q5                -0.0108      0.087     -0.124      0.901      -0.181       0.159\n",
      "black:agelt35            0.0295      0.045      0.659      0.510      -0.058       0.117\n",
      "black:agegt54           -0.0575      0.082     -0.701      0.484      -0.218       0.103\n",
      "black:durable           -0.0122      0.069     -0.176      0.860      -0.148       0.124\n",
      "black:lusd               0.0106      0.055      0.192      0.848      -0.097       0.119\n",
      "black:husd               0.1309      0.181      0.724      0.469      -0.224       0.486\n",
      "othrace:factor(dep)1     0.5857      0.223      2.631      0.009       0.149       1.022\n",
      "othrace:factor(dep)2    -0.1436      0.332     -0.433      0.665      -0.794       0.506\n",
      "othrace:q2              -0.4009      0.345     -1.162      0.245      -1.077       0.276\n",
      "othrace:q3               0.6009      0.236      2.544      0.011       0.138       1.064\n",
      "othrace:q4               0.2964      0.287      1.033      0.302      -0.266       0.859\n",
      "othrace:q5               0.2727      0.192      1.418      0.156      -0.104       0.650\n",
      "othrace:agelt35          0.0078      0.208      0.037      0.970      -0.400       0.416\n",
      "othrace:agegt54          0.2412      0.319      0.755      0.450      -0.385       0.867\n",
      "othrace:durable         -0.1464      0.214     -0.684      0.494      -0.566       0.273\n",
      "othrace:lusd             0.0925      0.207      0.447      0.655      -0.313       0.498\n",
      "othrace:husd            -0.8587      0.318     -2.702      0.007      -1.482      -0.236\n",
      "factor(dep)1:q2         -0.0779      0.186     -0.419      0.675      -0.442       0.286\n",
      "factor(dep)2:q2          0.1434      0.150      0.957      0.339      -0.150       0.437\n",
      "factor(dep)1:q3         -0.1494      0.185     -0.808      0.419      -0.512       0.213\n",
      "factor(dep)2:q3          0.1687      0.150      1.125      0.261      -0.125       0.463\n",
      "factor(dep)1:q4         -0.1595      0.184     -0.868      0.386      -0.520       0.201\n",
      "factor(dep)2:q4          0.1210      0.150      0.805      0.421      -0.174       0.416\n",
      "factor(dep)1:q5         -0.1177      0.183     -0.642      0.521      -0.477       0.242\n",
      "factor(dep)2:q5          0.1390      0.149      0.934      0.350      -0.153       0.431\n",
      "factor(dep)1:q6         -0.1531      0.195     -0.785      0.432      -0.535       0.229\n",
      "factor(dep)2:q6          0.1619      0.159      1.016      0.310      -0.150       0.474\n",
      "factor(dep)1:agelt35    -0.0036      0.047     -0.077      0.939      -0.095       0.088\n",
      "factor(dep)2:agelt35     0.0615      0.037      1.657      0.098      -0.011       0.134\n",
      "factor(dep)1:agegt54    -0.0883      0.065     -1.359      0.174      -0.216       0.039\n",
      "factor(dep)2:agegt54    -0.0785      0.151     -0.520      0.603      -0.374       0.217\n",
      "factor(dep)1:durable     0.0088      0.058      0.152      0.879      -0.104       0.122\n",
      "factor(dep)2:durable    -0.0924      0.051     -1.814      0.070      -0.192       0.007\n",
      "factor(dep)1:lusd        0.0359      0.050      0.710      0.478      -0.063       0.135\n",
      "factor(dep)2:lusd        0.0707      0.046      1.537      0.124      -0.019       0.161\n",
      "factor(dep)1:husd       -0.0181      0.054     -0.333      0.739      -0.124       0.088\n",
      "factor(dep)2:husd        0.0066      0.048      0.138      0.890      -0.087       0.100\n",
      "q2:agelt35               0.0306      0.133      0.231      0.818      -0.229       0.291\n",
      "q2:agegt54               0.0590      0.239      0.247      0.805      -0.410       0.528\n",
      "q2:durable               0.1371      0.175      0.783      0.434      -0.206       0.480\n",
      "q2:lusd                 -0.0531      0.072     -0.737      0.461      -0.194       0.088\n",
      "q2:husd                  0.0860      0.078      1.107      0.268      -0.066       0.238\n",
      "q3:agelt35               0.0303      0.132      0.229      0.819      -0.229       0.290\n",
      "q3:agegt54               0.0251      0.238      0.106      0.916      -0.441       0.491\n",
      "q3:durable               0.1905      0.175      1.089      0.276      -0.153       0.533\n",
      "q3:lusd                 -0.0706      0.071     -1.001      0.317      -0.209       0.068\n",
      "q3:husd                  0.0444      0.077      0.575      0.566      -0.107       0.196\n",
      "q4:agelt35               0.0723      0.132      0.546      0.585      -0.187       0.332\n",
      "q4:agegt54              -0.0193      0.237     -0.081      0.935      -0.484       0.445\n",
      "q4:durable               0.1229      0.176      0.699      0.485      -0.222       0.467\n",
      "q4:lusd                 -0.1132      0.071     -1.590      0.112      -0.253       0.026\n",
      "q4:husd                  0.0103      0.077      0.134      0.894      -0.141       0.162\n",
      "q5:agelt35               0.0465      0.132      0.353      0.724      -0.211       0.304\n",
      "q5:agegt54              -0.1165      0.236     -0.493      0.622      -0.579       0.346\n",
      "q5:durable               0.1025      0.173      0.591      0.555      -0.238       0.442\n",
      "q5:lusd                 -0.0357      0.070     -0.508      0.611      -0.174       0.102\n",
      "q5:husd                  0.0270      0.076      0.357      0.721      -0.121       0.175\n",
      "q6:agelt35               0.1099      0.140      0.787      0.431      -0.164       0.384\n",
      "q6:agegt54              -0.0174      0.248     -0.070      0.944      -0.503       0.469\n",
      "q6:durable               0.2161      0.183      1.179      0.239      -0.143       0.576\n",
      "agelt35:durable          0.0323      0.040      0.801      0.423      -0.047       0.111\n",
      "agelt35:lusd            -0.0092      0.035     -0.265      0.791      -0.077       0.059\n",
      "agelt35:husd            -0.0250      0.039     -0.641      0.521      -0.101       0.051\n",
      "agegt54:durable         -0.0155      0.065     -0.236      0.813      -0.144       0.113\n",
      "agegt54:lusd             0.0287      0.057      0.504      0.614      -0.083       0.140\n",
      "agegt54:husd            -0.0423      0.061     -0.694      0.488      -0.162       0.077\n",
      "durable:lusd            -0.0386      0.043     -0.901      0.368      -0.122       0.045\n",
      "durable:husd             0.0093      0.053      0.178      0.859      -0.094       0.112\n",
      "==============================================================================\n",
      "Omnibus:                    22320.003   Durbin-Watson:                   2.010\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              913.891\n",
      "Skew:                           0.316   Prob(JB):                    3.56e-199\n",
      "Kurtosis:                       1.158   Cond. No.                         236.\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors are heteroscedasticity robust (HC1)\n",
      "Number of regressors in the basic model: 103 \n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\edfra\\anaconda3\\lib\\site-packages\\statsmodels\\base\\model.py:1832: ValueWarning: covariance of constraints does not have full rank. The number of constraints is 102, but rank is 101\n",
      "  warnings.warn('covariance of constraints does not have full '\n"
     ]
    }
   ],
   "source": [
    "# model = \"T4~(female+black+othrace+C(dep)+q2+q3+q4+q5+q6+agelt35+agegt54+durable+lusd+husd)**2\"\n",
    "model_results = sm.OLS( y, X_vars ).fit().get_robustcov_results(cov_type = \"HC1\")\n",
    "\n",
    "print(model_results.summary())\n",
    "print( \"Number of regressors in the basic model:\",len(model_results.params), '\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cd07eb80",
   "metadata": {},
   "source": [
    "##### 1.3.2. classical linear regression adjustment (CRA)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "5b2bbac3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:           log_inuidur1   R-squared:                       0.001\n",
      "Model:                            OLS   Adj. R-squared:                  0.001\n",
      "Method:                 Least Squares   F-statistic:                     5.879\n",
      "Date:                Fri, 24 Sep 2021   Prob (F-statistic):             0.0154\n",
      "Time:                        17:40:20   Log-Likelihood:                -9344.7\n",
      "No. Observations:                5782   AIC:                         1.869e+04\n",
      "Df Residuals:                    5780   BIC:                         1.871e+04\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:                  HC1                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept      2.0568      0.021     98.158      0.000       2.016       2.098\n",
      "T2            -0.0788      0.033     -2.425      0.015      -0.143      -0.015\n",
      "==============================================================================\n",
      "Omnibus:                     3220.110   Durbin-Watson:                   1.978\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              593.120\n",
      "Skew:                          -0.541   Prob(JB):                    1.61e-129\n",
      "Kurtosis:                       1.865   Cond. No.                         2.47\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors are heteroscedasticity robust (HC1)\n",
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:               inuidur1   R-squared:                       0.056\n",
      "Model:                            OLS   Adj. R-squared:                  0.039\n",
      "Method:                 Least Squares   F-statistic:                     84.52\n",
      "Date:                Fri, 24 Sep 2021   Prob (F-statistic):               0.00\n",
      "Time:                        17:40:20   Log-Likelihood:                -9180.6\n",
      "No. Observations:                5782   AIC:                         1.857e+04\n",
      "Df Residuals:                    5678   BIC:                         1.926e+04\n",
      "Df Model:                         103                                         \n",
      "Covariance Type:                  HC1                                         \n",
      "========================================================================================\n",
      "                           coef    std err          t      P>|t|      [0.025      0.975]\n",
      "----------------------------------------------------------------------------------------\n",
      "(Intercept)              2.4472      0.358      6.834      0.000       1.745       3.149\n",
      "T2TRUE                  -0.0712      0.032     -2.222      0.026      -0.134      -0.008\n",
      "female                  -0.9428      0.283     -3.334      0.001      -1.497      -0.388\n",
      "black                   -0.6654      0.243     -2.738      0.006      -1.142      -0.189\n",
      "othrace                 -1.5495      0.731     -2.119      0.034      -2.983      -0.116\n",
      "factor(dep)1             0.2798      0.378      0.740      0.460      -0.462       1.022\n",
      "factor(dep)2             0.1678      0.368      0.456      0.648      -0.553       0.889\n",
      "q2                       0.0271      0.361      0.075      0.940      -0.680       0.734\n",
      "q3                      -0.3542      0.360     -0.984      0.325      -1.060       0.352\n",
      "q4                      -0.3380      0.361     -0.937      0.349      -1.045       0.369\n",
      "q5                      -0.1777      0.360     -0.494      0.621      -0.883       0.528\n",
      "q6                      -0.4456      0.361     -1.233      0.217      -1.154       0.263\n",
      "agelt35                 -0.3265      0.311     -1.050      0.294      -0.936       0.283\n",
      "agegt54                 -0.1552      0.480     -0.323      0.746      -1.095       0.785\n",
      "durable                 -1.0152      0.445     -2.282      0.023      -1.887      -0.143\n",
      "lusd                    -0.1194      0.174     -0.684      0.494      -0.461       0.223\n",
      "husd                     0.0703      0.188      0.374      0.708      -0.298       0.438\n",
      "female:black            -0.2840      0.118     -2.403      0.016      -0.516      -0.052\n",
      "female:othrace           0.4212      0.488      0.863      0.388      -0.536       1.378\n",
      "female:factor(dep)1     -0.0852      0.110     -0.776      0.438      -0.300       0.130\n",
      "female:factor(dep)2      0.0192      0.096      0.200      0.842      -0.169       0.207\n",
      "female:q2                0.7579      0.282      2.691      0.007       0.206       1.310\n",
      "female:q3                1.1132      0.280      3.969      0.000       0.563       1.663\n",
      "female:q4                1.0476      0.280      3.737      0.000       0.498       1.597\n",
      "female:q5                0.9392      0.280      3.357      0.001       0.391       1.488\n",
      "female:q6                1.0943      0.305      3.586      0.000       0.496       1.693\n",
      "female:agelt35           0.0897      0.072      1.250      0.211      -0.051       0.230\n",
      "female:agegt54           0.1052      0.119      0.882      0.378      -0.129       0.339\n",
      "female:durable           0.1479      0.105      1.414      0.157      -0.057       0.353\n",
      "female:lusd              0.1040      0.080      1.294      0.196      -0.054       0.262\n",
      "female:husd              0.0405      0.087      0.465      0.642      -0.130       0.211\n",
      "black:factor(dep)1       0.2551      0.178      1.435      0.151      -0.093       0.603\n",
      "black:factor(dep)2      -0.0027      0.169     -0.016      0.987      -0.333       0.328\n",
      "black:q2                 0.1579      0.254      0.621      0.535      -0.340       0.656\n",
      "black:q3                 0.3137      0.242      1.295      0.195      -0.161       0.788\n",
      "black:q4                 0.4071      0.253      1.608      0.108      -0.089       0.903\n",
      "black:q5                 0.4780      0.242      1.972      0.049       0.003       0.953\n",
      "black:agelt35           -0.0328      0.120     -0.273      0.785      -0.268       0.202\n",
      "black:agegt54            0.2404      0.215      1.118      0.264      -0.181       0.662\n",
      "black:durable            0.1605      0.182      0.883      0.378      -0.196       0.517\n",
      "black:lusd               0.0977      0.140      0.699      0.485      -0.176       0.372\n",
      "black:husd               0.5227      0.471      1.111      0.267      -0.400       1.445\n",
      "othrace:factor(dep)1     1.1662      0.569      2.050      0.040       0.051       2.281\n",
      "othrace:factor(dep)2    -0.7687      0.688     -1.117      0.264      -2.118       0.580\n",
      "othrace:q2               1.5108      0.755      2.000      0.046       0.030       2.992\n",
      "othrace:q3               0.2916      0.401      0.727      0.467      -0.495       1.078\n",
      "othrace:q4               0.9423      0.522      1.806      0.071      -0.080       1.965\n",
      "othrace:q5               0.9633      0.451      2.137      0.033       0.079       1.847\n",
      "othrace:agelt35         -0.1902      0.574     -0.331      0.740      -1.316       0.935\n",
      "othrace:agegt54         -1.5320      0.777     -1.971      0.049      -3.056      -0.008\n",
      "othrace:durable          1.9037      0.298      6.379      0.000       1.319       2.489\n",
      "othrace:lusd            -0.1288      0.385     -0.335      0.738      -0.883       0.625\n",
      "othrace:husd             0.6131      0.632      0.970      0.332      -0.626       1.853\n",
      "factor(dep)1:q2         -0.0578      0.377     -0.154      0.878      -0.796       0.681\n",
      "factor(dep)2:q2         -0.0868      0.366     -0.237      0.813      -0.805       0.631\n",
      "factor(dep)1:q3         -0.1448      0.377     -0.384      0.701      -0.883       0.594\n",
      "factor(dep)2:q3         -0.0664      0.366     -0.181      0.856      -0.784       0.651\n",
      "factor(dep)1:q4         -0.2323      0.376     -0.618      0.537      -0.969       0.504\n",
      "factor(dep)2:q4         -0.0189      0.366     -0.052      0.959      -0.737       0.699\n",
      "factor(dep)1:q5         -0.1367      0.375     -0.365      0.715      -0.871       0.598\n",
      "factor(dep)2:q5         -0.3474      0.366     -0.950      0.342      -1.065       0.370\n",
      "factor(dep)1:q6         -0.0754      0.424     -0.178      0.859      -0.906       0.755\n",
      "factor(dep)2:q6          0.1670      0.386      0.432      0.665      -0.590       0.924\n",
      "factor(dep)1:agelt35     0.0296      0.111      0.266      0.790      -0.188       0.247\n",
      "factor(dep)2:agelt35     0.0604      0.088      0.684      0.494      -0.113       0.234\n",
      "factor(dep)1:agegt54    -0.0654      0.158     -0.414      0.679      -0.375       0.244\n",
      "factor(dep)2:agegt54     0.2389      0.350      0.682      0.495      -0.448       0.926\n",
      "factor(dep)1:durable     0.2490      0.139      1.791      0.073      -0.024       0.522\n",
      "factor(dep)2:durable     0.1155      0.121      0.955      0.340      -0.122       0.353\n",
      "factor(dep)1:lusd       -0.1891      0.120     -1.578      0.115      -0.424       0.046\n",
      "factor(dep)2:lusd        0.0100      0.108      0.092      0.926      -0.202       0.222\n",
      "factor(dep)1:husd        0.0210      0.130      0.162      0.871      -0.234       0.276\n",
      "factor(dep)2:husd        0.0644      0.112      0.575      0.565      -0.155       0.284\n",
      "q2:agelt35               0.1075      0.311      0.346      0.729      -0.501       0.716\n",
      "q2:agegt54               0.2089      0.479      0.436      0.663      -0.730       1.148\n",
      "q2:durable               0.4369      0.445      0.983      0.326      -0.435       1.309\n",
      "q2:lusd                 -0.0452      0.178     -0.255      0.799      -0.393       0.303\n",
      "q2:husd                 -0.2224      0.187     -1.191      0.234      -0.588       0.144\n",
      "q3:agelt35               0.1805      0.311      0.581      0.561      -0.428       0.789\n",
      "q3:agegt54               0.5557      0.475      1.169      0.242      -0.376       1.487\n",
      "q3:durable               0.8396      0.444      1.889      0.059      -0.032       1.711\n",
      "q3:lusd                 -0.0160      0.175     -0.091      0.927      -0.359       0.327\n",
      "q3:husd                 -0.1131      0.185     -0.610      0.542      -0.476       0.250\n",
      "q4:agelt35               0.1183      0.310      0.381      0.703      -0.490       0.727\n",
      "q4:agegt54               0.3742      0.473      0.791      0.429      -0.553       1.301\n",
      "q4:durable               0.7434      0.447      1.663      0.096      -0.133       1.620\n",
      "q4:lusd                 -0.0222      0.178     -0.125      0.901      -0.371       0.326\n",
      "q4:husd                 -0.1848      0.186     -0.993      0.321      -0.550       0.180\n",
      "q5:agelt35              -0.0348      0.309     -0.112      0.910      -0.642       0.572\n",
      "q5:agegt54               0.2173      0.474      0.458      0.647      -0.712       1.147\n",
      "q5:durable               0.8554      0.444      1.928      0.054      -0.014       1.725\n",
      "q5:lusd                 -0.1843      0.177     -1.043      0.297      -0.531       0.162\n",
      "q5:husd                 -0.2742      0.184     -1.486      0.137      -0.636       0.088\n",
      "q6:agelt35               0.1822      0.333      0.547      0.584      -0.471       0.835\n",
      "q6:agegt54               0.6639      0.504      1.318      0.188      -0.324       1.652\n",
      "q6:durable               1.1467      0.466      2.462      0.014       0.234       2.060\n",
      "agelt35:durable          0.0130      0.097      0.134      0.894      -0.178       0.204\n",
      "agelt35:lusd            -0.0561      0.083     -0.679      0.497      -0.218       0.106\n",
      "agelt35:husd             0.0175      0.092      0.190      0.849      -0.162       0.197\n",
      "agegt54:durable         -0.0134      0.155     -0.086      0.931      -0.318       0.291\n",
      "agegt54:lusd            -0.0236      0.130     -0.182      0.856      -0.278       0.231\n",
      "agegt54:husd            -0.2739      0.144     -1.906      0.057      -0.556       0.008\n",
      "durable:lusd             0.2916      0.103      2.821      0.005       0.089       0.494\n",
      "durable:husd             0.3027      0.124      2.437      0.015       0.059       0.546\n",
      "==============================================================================\n",
      "Omnibus:                     1646.671   Durbin-Watson:                   2.004\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              492.421\n",
      "Skew:                          -0.503   Prob(JB):                    1.18e-107\n",
      "Kurtosis:                       1.984   Cond. No.                         245.\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors are heteroscedasticity robust (HC1)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\edfra\\anaconda3\\lib\\site-packages\\statsmodels\\base\\model.py:1832: ValueWarning: covariance of constraints does not have full rank. The number of constraints is 103, but rank is 102\n",
      "  warnings.warn('covariance of constraints does not have full '\n"
     ]
    }
   ],
   "source": [
    "# model specifications\n",
    "# take log of inuidur1\n",
    "Penn[\"log_inuidur1\"] = np.log( Penn[\"inuidur1\"] ) \n",
    "log_inuidur1 = pd.DataFrame(np.log( Penn[\"inuidur1\"] ) ).reset_index( drop = True )\n",
    "\n",
    "# no adjustment (2-sample approach)\n",
    "formula_cl = 'log_inuidur1 ~ T2'\n",
    "\n",
    "# adding controls\n",
    "# formula_cra = 'log_inuidur1 ~ T4 + (female+black+othrace+dep+q2+q3+q4+q5+q6+agelt35+agegt54+durable+lusd+husd)**2'\n",
    "# Omitted dummies: q1, nondurable, muld\n",
    "\n",
    "ols_cl = smf.ols( formula = formula_cl, data = Penn ).fit().get_robustcov_results(cov_type = \"HC1\")\n",
    "\n",
    "#getting data\n",
    "# Get data from R\n",
    "result = pyreadr.read_r('../data/ols_cra_reg.RData')\n",
    "X_vars = result['X1']\n",
    "\n",
    "ols_cra = sm.OLS( log_inuidur1, X_vars ).fit().get_robustcov_results(cov_type = \"HC1\")\n",
    "\n",
    "# Results \n",
    "print(ols_cl.summary())\n",
    "print(ols_cra.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d8cc47f1",
   "metadata": {},
   "source": [
    "##### 1.3.3. interactive regression adjustment (IRA)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "78f2e290",
   "metadata": {},
   "outputs": [],
   "source": [
    "# create Y variable \n",
    "log_inuidur1 = pd.DataFrame(np.log( Penn[\"inuidur1\"] )).reset_index( drop = True )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "ddacb497",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:               inuidur1   R-squared:                       0.073\n",
      "Model:                            OLS   Adj. R-squared:                  0.039\n",
      "Method:                 Least Squares   F-statistic:                     35.02\n",
      "Date:                Fri, 24 Sep 2021   Prob (F-statistic):               0.00\n",
      "Time:                        17:40:20   Log-Likelihood:                -9129.1\n",
      "No. Observations:                5782   AIC:                         1.866e+04\n",
      "Df Residuals:                    5579   BIC:                         2.002e+04\n",
      "Df Model:                         202                                         \n",
      "Covariance Type:                  HC1                                         \n",
      "================================================================================================\n",
      "                                   coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------------------------\n",
      "(Intercept)                      2.0557      0.021     99.243      0.000       2.015       2.096\n",
      "T2TRUE                          -0.0699      0.032     -2.179      0.029      -0.133      -0.007\n",
      "Xfemale                         -0.6663      0.408     -1.633      0.103      -1.466       0.134\n",
      "Xblack                          -0.8635      0.297     -2.908      0.004      -1.446      -0.281\n",
      "Xothrace                        -3.8177      0.937     -4.076      0.000      -5.654      -1.982\n",
      "Xfactor(dep)1                    0.0359      0.648      0.055      0.956      -1.234       1.306\n",
      "Xfactor(dep)2                    0.2118      0.451      0.469      0.639      -0.673       1.096\n",
      "Xq2                             -0.2546      0.455     -0.559      0.576      -1.147       0.638\n",
      "Xq3                             -0.6212      0.455     -1.365      0.172      -1.513       0.271\n",
      "Xq4                             -0.4799      0.456     -1.052      0.293      -1.374       0.414\n",
      "Xq5                             -0.3719      0.454     -0.819      0.413      -1.262       0.518\n",
      "Xq6                             -0.6770      0.452     -1.497      0.134      -1.563       0.209\n",
      "Xagelt35                        -0.6777      0.409     -1.655      0.098      -1.480       0.125\n",
      "Xagegt54                        -0.3041      0.701     -0.434      0.664      -1.678       1.069\n",
      "Xdurable                        -0.8380      0.596     -1.405      0.160      -2.007       0.331\n",
      "Xlusd                           -0.0995      0.228     -0.436      0.662      -0.546       0.347\n",
      "Xhusd                           -0.0626      0.235     -0.267      0.790      -0.523       0.398\n",
      "Xfemale:black                   -0.2155      0.151     -1.432      0.152      -0.511       0.080\n",
      "Xfemale:othrace                  0.5994      0.563      1.066      0.287      -0.503       1.702\n",
      "Xfemale:factor(dep)1            -0.1734      0.142     -1.222      0.222      -0.452       0.105\n",
      "Xfemale:factor(dep)2             0.2166      0.126      1.715      0.086      -0.031       0.464\n",
      "Xfemale:q2                       0.3919      0.407      0.964      0.335      -0.405       1.189\n",
      "Xfemale:q3                       0.6850      0.406      1.686      0.092      -0.112       1.482\n",
      "Xfemale:q4                       0.7214      0.406      1.776      0.076      -0.075       1.518\n",
      "Xfemale:q5                       0.5658      0.405      1.398      0.162      -0.228       1.359\n",
      "Xfemale:q6                       0.9082      0.430      2.110      0.035       0.064       1.752\n",
      "Xfemale:agelt35                  0.1701      0.095      1.789      0.074      -0.016       0.356\n",
      "Xfemale:agegt54                  0.2358      0.160      1.472      0.141      -0.078       0.550\n",
      "Xfemale:durable                  0.0974      0.138      0.708      0.479      -0.172       0.367\n",
      "Xfemale:lusd                     0.0720      0.107      0.676      0.499      -0.137       0.281\n",
      "Xfemale:husd                     0.0610      0.116      0.524      0.601      -0.167       0.289\n",
      "Xblack:factor(dep)1              0.1090      0.228      0.477      0.633      -0.339       0.557\n",
      "Xblack:factor(dep)2             -0.2007      0.228     -0.878      0.380      -0.649       0.247\n",
      "Xblack:q2                        0.4267      0.321      1.331      0.183      -0.202       1.055\n",
      "Xblack:q3                        0.3299      0.303      1.089      0.276      -0.264       0.924\n",
      "Xblack:q4                        0.5443      0.311      1.753      0.080      -0.064       1.153\n",
      "Xblack:q5                        0.6597      0.300      2.198      0.028       0.071       1.248\n",
      "Xblack:agelt35                   0.0830      0.158      0.524      0.600      -0.227       0.393\n",
      "Xblack:agegt54                   0.4230      0.255      1.660      0.097      -0.076       0.922\n",
      "Xblack:durable                   0.4681      0.227      2.058      0.040       0.022       0.914\n",
      "Xblack:lusd                      0.3086      0.184      1.674      0.094      -0.053       0.670\n",
      "Xblack:husd                      0.5498      0.660      0.834      0.405      -0.743       1.843\n",
      "Xothrace:factor(dep)1            2.5202      1.855      1.359      0.174      -1.116       6.157\n",
      "Xothrace:factor(dep)2            1.1889      0.573      2.074      0.038       0.065       2.312\n",
      "Xothrace:q2                      2.0826      0.608      3.423      0.001       0.890       3.275\n",
      "Xothrace:q3                      0.9373      0.660      1.420      0.156      -0.357       2.232\n",
      "Xothrace:q4                      3.8597      0.881      4.384      0.000       2.134       5.586\n",
      "Xothrace:q5                      0.9712      0.553      1.758      0.079      -0.112       2.054\n",
      "Xothrace:agelt35                 2.2480      0.915      2.456      0.014       0.453       4.043\n",
      "Xothrace:agegt54                -4.1490      0.482     -8.610      0.000      -5.094      -3.204\n",
      "Xothrace:durable                 1.7506      0.370      4.725      0.000       1.024       2.477\n",
      "Xothrace:lusd                   -0.6944      0.495     -1.402      0.161      -1.665       0.276\n",
      "Xothrace:husd                    4.7889      0.818      5.855      0.000       3.185       6.392\n",
      "Xfactor(dep)1:q2                 0.2339      0.642      0.365      0.715      -1.024       1.492\n",
      "Xfactor(dep)2:q2                -0.2177      0.448     -0.486      0.627      -1.096       0.661\n",
      "Xfactor(dep)1:q3                 0.1543      0.644      0.240      0.811      -1.108       1.416\n",
      "Xfactor(dep)2:q3                -0.2545      0.450     -0.565      0.572      -1.137       0.628\n",
      "Xfactor(dep)1:q4                -0.0239      0.642     -0.037      0.970      -1.283       1.235\n",
      "Xfactor(dep)2:q4                -0.1619      0.451     -0.359      0.720      -1.046       0.722\n",
      "Xfactor(dep)1:q5                 0.1396      0.638      0.219      0.827      -1.112       1.391\n",
      "Xfactor(dep)2:q5                -0.5626      0.449     -1.253      0.210      -1.443       0.317\n",
      "Xfactor(dep)1:q6                 0.4833      0.680      0.710      0.477      -0.850       1.817\n",
      "Xfactor(dep)2:q6                -0.0048      0.477     -0.010      0.992      -0.941       0.931\n",
      "Xfactor(dep)1:agelt35            0.0508      0.149      0.340      0.734      -0.242       0.343\n",
      "Xfactor(dep)2:agelt35            0.1192      0.116      1.026      0.305      -0.109       0.347\n",
      "Xfactor(dep)1:agegt54            0.0366      0.205      0.178      0.858      -0.366       0.439\n",
      "Xfactor(dep)2:agegt54            0.2231      0.421      0.530      0.596      -0.602       1.048\n",
      "Xfactor(dep)1:durable            0.3397      0.184      1.845      0.065      -0.021       0.701\n",
      "Xfactor(dep)2:durable            0.1147      0.153      0.748      0.454      -0.186       0.415\n",
      "Xfactor(dep)1:lusd              -0.2604      0.162     -1.609      0.108      -0.578       0.057\n",
      "Xfactor(dep)2:lusd               0.1151      0.140      0.823      0.411      -0.159       0.389\n",
      "Xfactor(dep)1:husd              -0.0428      0.166     -0.258      0.796      -0.368       0.282\n",
      "Xfactor(dep)2:husd               0.0180      0.151      0.119      0.905      -0.278       0.314\n",
      "Xq2:agelt35                      0.5007      0.410      1.222      0.222      -0.302       1.304\n",
      "Xq2:agegt54                      0.4322      0.698      0.619      0.536      -0.936       1.800\n",
      "Xq2:durable                      0.2615      0.593      0.441      0.659      -0.901       1.424\n",
      "Xq2:lusd                        -0.0675      0.231     -0.292      0.770      -0.520       0.385\n",
      "Xq2:husd                        -0.1818      0.234     -0.778      0.437      -0.640       0.276\n",
      "Xq3:agelt35                      0.5137      0.410      1.254      0.210      -0.290       1.317\n",
      "Xq3:agegt54                      0.7246      0.696      1.042      0.298      -0.639       2.088\n",
      "Xq3:durable                      0.7224      0.592      1.221      0.222      -0.438       1.883\n",
      "Xq3:lusd                        -0.0014      0.230     -0.006      0.995      -0.453       0.450\n",
      "Xq3:husd                         0.0989      0.232      0.427      0.669      -0.355       0.553\n",
      "Xq4:agelt35                      0.4048      0.410      0.988      0.323      -0.398       1.208\n",
      "Xq4:agegt54                      0.2469      0.694      0.356      0.722      -1.113       1.607\n",
      "Xq4:durable                      0.5938      0.598      0.993      0.321      -0.578       1.766\n",
      "Xq4:lusd                        -0.1686      0.232     -0.726      0.468      -0.624       0.287\n",
      "Xq4:husd                        -0.1451      0.235     -0.618      0.537      -0.605       0.315\n",
      "Xq5:agelt35                      0.2160      0.408      0.529      0.597      -0.584       1.016\n",
      "Xq5:agegt54                      0.0996      0.692      0.144      0.885      -1.256       1.456\n",
      "Xq5:durable                      0.8536      0.590      1.446      0.148      -0.304       2.011\n",
      "Xq5:lusd                        -0.2593      0.234     -1.108      0.268      -0.718       0.199\n",
      "Xq5:husd                        -0.1736      0.232     -0.749      0.454      -0.628       0.280\n",
      "Xq6:agelt35                      0.3824      0.434      0.881      0.379      -0.469       1.234\n",
      "Xq6:agegt54                      0.8362      0.728      1.149      0.251      -0.591       2.263\n",
      "Xq6:durable                      1.0289      0.618      1.664      0.096      -0.183       2.241\n",
      "Xagelt35:durable                -0.0027      0.126     -0.021      0.983      -0.249       0.243\n",
      "Xagelt35:lusd                   -0.0574      0.109     -0.525      0.600      -0.272       0.157\n",
      "Xagelt35:husd                    0.0545      0.126      0.434      0.664      -0.192       0.301\n",
      "Xagegt54:durable                -0.1239      0.207     -0.598      0.550      -0.530       0.283\n",
      "Xagegt54:lusd                    0.0912      0.176      0.519      0.604      -0.253       0.436\n",
      "Xagegt54:husd                   -0.3146      0.189     -1.662      0.097      -0.686       0.056\n",
      "Xdurable:lusd                    0.2634      0.138      1.911      0.056      -0.007       0.533\n",
      "Xdurable:husd                    0.3138      0.158      1.984      0.047       0.004       0.624\n",
      "T2TRUE:Xfemale                  -0.6598      0.550     -1.200      0.230      -1.738       0.418\n",
      "T2TRUE:Xblack                    0.4526      0.510      0.887      0.375      -0.548       1.453\n",
      "T2TRUE:Xothrace                  2.2115      1.102      2.007      0.045       0.051       4.372\n",
      "T2TRUE:Xfactor(dep)1             0.7636      0.785      0.972      0.331      -0.776       2.303\n",
      "T2TRUE:Xfactor(dep)2            -0.0512      0.779     -0.066      0.948      -1.578       1.475\n",
      "T2TRUE:Xq2                       0.8969      0.737      1.217      0.224      -0.548       2.342\n",
      "T2TRUE:Xq3                       0.8962      0.736      1.218      0.223      -0.546       2.338\n",
      "T2TRUE:Xq4                       0.5890      0.736      0.800      0.424      -0.854       2.032\n",
      "T2TRUE:Xq5                       0.7173      0.735      0.976      0.329      -0.723       2.158\n",
      "T2TRUE:Xq6                       0.7812      0.743      1.051      0.293      -0.676       2.239\n",
      "T2TRUE:Xagelt35                  1.1233      0.637      1.762      0.078      -0.126       2.373\n",
      "T2TRUE:Xagegt54                  0.4143      0.871      0.475      0.635      -1.294       2.123\n",
      "T2TRUE:Xdurable                  0.0699      0.851      0.082      0.935      -1.598       1.737\n",
      "T2TRUE:Xlusd                    -0.0864      0.361     -0.240      0.811      -0.794       0.621\n",
      "T2TRUE:Xhusd                     0.2846      0.399      0.713      0.476      -0.497       1.067\n",
      "T2TRUE:Xfemale:black            -0.2036      0.243     -0.837      0.403      -0.681       0.274\n",
      "T2TRUE:Xfemale:othrace          -0.9119      0.580     -1.572      0.116      -2.049       0.225\n",
      "T2TRUE:Xfemale:factor(dep)1      0.1813      0.220      0.826      0.409      -0.249       0.612\n",
      "T2TRUE:Xfemale:factor(dep)2     -0.4690      0.199     -2.360      0.018      -0.859      -0.079\n",
      "T2TRUE:Xfemale:q2                0.8485      0.549      1.545      0.122      -0.228       1.925\n",
      "T2TRUE:Xfemale:q3                1.0429      0.546      1.910      0.056      -0.028       2.113\n",
      "T2TRUE:Xfemale:q4                0.7825      0.546      1.434      0.152      -0.287       1.852\n",
      "T2TRUE:Xfemale:q5                0.8994      0.545      1.651      0.099      -0.169       1.967\n",
      "T2TRUE:Xfemale:q6                0.4086      0.604      0.677      0.499      -0.775       1.592\n",
      "T2TRUE:Xfemale:agelt35          -0.1861      0.146     -1.276      0.202      -0.472       0.100\n",
      "T2TRUE:Xfemale:agegt54          -0.3821      0.244     -1.564      0.118      -0.861       0.097\n",
      "T2TRUE:Xfemale:durable           0.1104      0.215      0.514      0.607      -0.310       0.531\n",
      "T2TRUE:Xfemale:lusd              0.1061      0.163      0.650      0.515      -0.214       0.426\n",
      "T2TRUE:Xfemale:husd             -0.0426      0.179     -0.239      0.811      -0.393       0.307\n",
      "T2TRUE:Xblack:factor(dep)1       0.3636      0.350      1.038      0.299      -0.323       1.050\n",
      "T2TRUE:Xblack:factor(dep)2       0.4284      0.338      1.266      0.206      -0.235       1.092\n",
      "T2TRUE:Xblack:q2                -0.5878      0.529     -1.112      0.266      -1.625       0.449\n",
      "T2TRUE:Xblack:q3                -0.0625      0.510     -0.123      0.902      -1.062       0.937\n",
      "T2TRUE:Xblack:q4                -0.4010      0.537     -0.747      0.455      -1.453       0.651\n",
      "T2TRUE:Xblack:q5                -0.4359      0.509     -0.856      0.392      -1.434       0.562\n",
      "T2TRUE:Xblack:agelt35           -0.2159      0.242     -0.893      0.372      -0.690       0.258\n",
      "T2TRUE:Xblack:agegt54           -0.2617      0.487     -0.538      0.591      -1.216       0.692\n",
      "T2TRUE:Xblack:durable           -0.8365      0.350     -2.387      0.017      -1.524      -0.149\n",
      "T2TRUE:Xblack:lusd              -0.4265      0.280     -1.525      0.127      -0.975       0.122\n",
      "T2TRUE:Xblack:husd              -0.1958      0.891     -0.220      0.826      -1.943       1.551\n",
      "T2TRUE:Xothrace:factor(dep)1     0.4788      1.895      0.253      0.801      -3.236       4.193\n",
      "T2TRUE:Xothrace:factor(dep)2    -4.4514      0.782     -5.695      0.000      -5.984      -2.919\n",
      "T2TRUE:Xothrace:q3              -0.7826      0.915     -0.856      0.392      -2.576       1.010\n",
      "T2TRUE:Xothrace:q4              -2.4173      1.144     -2.113      0.035      -4.660      -0.174\n",
      "T2TRUE:Xothrace:q5               0.7621      0.796      0.958      0.338      -0.798       2.322\n",
      "T2TRUE:Xothrace:agelt35         -3.8313      0.954     -4.017      0.000      -5.701      -1.962\n",
      "T2TRUE:Xothrace:durable          1.5659      0.462      3.388      0.001       0.660       2.472\n",
      "T2TRUE:Xothrace:lusd             2.0930      0.514      4.076      0.000       1.086       3.100\n",
      "T2TRUE:Xfactor(dep)1:q2         -0.8318      0.778     -1.069      0.285      -2.358       0.694\n",
      "T2TRUE:Xfactor(dep)2:q2          0.2249      0.778      0.289      0.773      -1.300       1.750\n",
      "T2TRUE:Xfactor(dep)1:q3         -0.8573      0.780     -1.099      0.272      -2.387       0.672\n",
      "T2TRUE:Xfactor(dep)2:q3          0.3587      0.775      0.463      0.644      -1.161       1.878\n",
      "T2TRUE:Xfactor(dep)1:q4         -0.6286      0.776     -0.810      0.418      -2.151       0.894\n",
      "T2TRUE:Xfactor(dep)2:q4          0.2390      0.774      0.309      0.758      -1.279       1.757\n",
      "T2TRUE:Xfactor(dep)1:q5         -0.8385      0.775     -1.082      0.279      -2.358       0.681\n",
      "T2TRUE:Xfactor(dep)2:q5          0.4285      0.774      0.553      0.580      -1.090       1.947\n",
      "T2TRUE:Xfactor(dep)1:q6         -1.5382      0.893     -1.722      0.085      -3.289       0.213\n",
      "T2TRUE:Xfactor(dep)2:q6          0.2847      0.818      0.348      0.728      -1.319       1.888\n",
      "T2TRUE:Xfactor(dep)1:agelt35    -0.0778      0.224     -0.347      0.729      -0.518       0.362\n",
      "T2TRUE:Xfactor(dep)2:agelt35    -0.1017      0.180     -0.564      0.573      -0.455       0.252\n",
      "T2TRUE:Xfactor(dep)1:agegt54    -0.2825      0.324     -0.873      0.383      -0.917       0.352\n",
      "T2TRUE:Xfactor(dep)2:agegt54     0.3793      0.571      0.664      0.507      -0.741       1.500\n",
      "T2TRUE:Xfactor(dep)1:durable    -0.2186      0.280     -0.779      0.436      -0.768       0.331\n",
      "T2TRUE:Xfactor(dep)2:durable    -0.0951      0.260     -0.366      0.715      -0.605       0.414\n",
      "T2TRUE:Xfactor(dep)1:lusd        0.2161      0.243      0.888      0.374      -0.261       0.693\n",
      "T2TRUE:Xfactor(dep)2:lusd       -0.2071      0.222     -0.933      0.351      -0.642       0.228\n",
      "T2TRUE:Xfactor(dep)1:husd        0.1232      0.268      0.460      0.646      -0.402       0.649\n",
      "T2TRUE:Xfactor(dep)2:husd        0.1580      0.230      0.686      0.493      -0.293       0.609\n",
      "T2TRUE:Xq2:agelt35              -1.2408      0.636     -1.952      0.051      -2.487       0.005\n",
      "T2TRUE:Xq2:agegt54              -0.6020      0.871     -0.691      0.489      -2.309       1.105\n",
      "T2TRUE:Xq2:durable              -0.0778      0.855     -0.091      0.928      -1.754       1.598\n",
      "T2TRUE:Xq2:lusd                  0.0508      0.367      0.138      0.890      -0.669       0.771\n",
      "T2TRUE:Xq2:husd                 -0.0920      0.395     -0.233      0.816      -0.866       0.682\n",
      "T2TRUE:Xq3:agelt35              -1.1278      0.636     -1.775      0.076      -2.374       0.118\n",
      "T2TRUE:Xq3:agegt54              -0.5411      0.862     -0.628      0.530      -2.231       1.149\n",
      "T2TRUE:Xq3:durable              -0.1956      0.855     -0.229      0.819      -1.872       1.481\n",
      "T2TRUE:Xq3:lusd                 -0.0740      0.361     -0.205      0.838      -0.781       0.633\n",
      "T2TRUE:Xq3:husd                 -0.5532      0.391     -1.414      0.157      -1.320       0.214\n",
      "T2TRUE:Xq4:agelt35              -0.9926      0.634     -1.565      0.118      -2.236       0.251\n",
      "T2TRUE:Xq4:agegt54               0.2122      0.852      0.249      0.803      -1.458       1.883\n",
      "T2TRUE:Xq4:durable              -0.1188      0.862     -0.138      0.890      -1.808       1.571\n",
      "T2TRUE:Xq4:lusd                  0.3042      0.366      0.831      0.406      -0.414       1.022\n",
      "T2TRUE:Xq4:husd                 -0.0828      0.393     -0.211      0.833      -0.852       0.687\n",
      "T2TRUE:Xq5:agelt35              -0.9263      0.633     -1.464      0.143      -2.167       0.314\n",
      "T2TRUE:Xq5:agegt54               0.2188      0.862      0.254      0.800      -1.471       1.909\n",
      "T2TRUE:Xq5:durable              -0.4451      0.852     -0.523      0.601      -2.115       1.225\n",
      "T2TRUE:Xq5:lusd                  0.0896      0.364      0.246      0.806      -0.625       0.804\n",
      "T2TRUE:Xq5:husd                 -0.2456      0.391     -0.629      0.529      -1.011       0.520\n",
      "T2TRUE:Xq6:agelt35              -0.7320      0.684     -1.070      0.285      -2.073       0.609\n",
      "T2TRUE:Xq6:agegt54              -0.4355      0.935     -0.466      0.641      -2.268       1.397\n",
      "T2TRUE:Xq6:durable              -0.1469      0.905     -0.162      0.871      -1.921       1.627\n",
      "T2TRUE:Xagelt35:durable          0.0216      0.201      0.108      0.914      -0.371       0.415\n",
      "T2TRUE:Xagelt35:lusd             0.0680      0.170      0.401      0.688      -0.264       0.400\n",
      "T2TRUE:Xagelt35:husd            -0.0587      0.186     -0.316      0.752      -0.423       0.305\n",
      "T2TRUE:Xagegt54:durable          0.1732      0.318      0.545      0.586      -0.450       0.796\n",
      "T2TRUE:Xagegt54:lusd            -0.1245      0.267     -0.467      0.641      -0.647       0.398\n",
      "T2TRUE:Xagegt54:husd             0.2899      0.296      0.979      0.328      -0.291       0.870\n",
      "T2TRUE:Xdurable:lusd             0.1281      0.212      0.605      0.545      -0.287       0.543\n",
      "T2TRUE:Xdurable:husd             0.0133      0.255      0.052      0.958      -0.487       0.514\n",
      "==============================================================================\n",
      "Omnibus:                     1349.401   Durbin-Watson:                   1.997\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              465.384\n",
      "Skew:                          -0.496   Prob(JB):                    8.77e-102\n",
      "Kurtosis:                       2.027   Cond. No.                         714.\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors are heteroscedasticity robust (HC1)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\edfra\\anaconda3\\lib\\site-packages\\statsmodels\\base\\model.py:1832: ValueWarning: covariance of constraints does not have full rank. The number of constraints is 202, but rank is 188\n",
      "  warnings.warn('covariance of constraints does not have full '\n"
     ]
    }
   ],
   "source": [
    "# Reset index to estimation\n",
    "# Get data from R\n",
    "result = pyreadr.read_r('../data/ols_ira_reg.RData')\n",
    "X_vars = result['S1']\n",
    "\n",
    "ols_ira = sm.OLS( log_inuidur1, X_vars ).fit().get_robustcov_results(cov_type = \"HC1\")\n",
    "\n",
    "# Results \n",
    "print(ols_ira.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9a791d17",
   "metadata": {},
   "source": [
    "##### 1.3.4. interactive regression adjustment (IRA) using Lasso. I already fixed the problems in the Python code so please check again the Python script for the solution. For this now I used another package called hdmpy, you can see how to install it in this link. If you don´t want to use this package (which will give you the exact results as it is in R) just use the lasso from scikit-learn."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "54c120b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "import hdmpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "38b1a9a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get data from R\n",
    "result = pyreadr.read_r('../data/rlasso_ira_reg.RData')\n",
    "X_vars = result['S']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "1163c155",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Coef.</th>\n",
       "      <th>Std.Err.</th>\n",
       "      <th>t</th>\n",
       "      <th>P&gt;|t|</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>T2</th>\n",
       "      <td>-0.074573</td>\n",
       "      <td>0.032104</td>\n",
       "      <td>-2.322847</td>\n",
       "      <td>0.020187</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Coef.  Std.Err.         t     P>|t|\n",
       "T2 -0.074573  0.032104 -2.322847  0.020187"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result = hdmpy.rlassoEffects( X_vars, log_inuidur1, index = 0 )       \n",
    "\n",
    "rlasso_ira = pd.DataFrame(np.array( (result.res['coefficients'][0] , result.res['se'][0] , \\\n",
    "           result.res['t'][0] , result.res['pval'][0] ) ).reshape(1, 4) , columns = ['Coef.' , \\\n",
    "                            \"Std.Err.\" , \"t\" , 'P>|t|'] , index = ['T2'])\n",
    "rlasso_ira"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "9b522b48",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\\begin{tabular}{lrrrr}\n",
      "\\toprule\n",
      "{} &      \\$CL\\$ &     \\$CRA\\$ &     \\$IRA\\$ &  \\$IRA Lasso\\$ \\\\\n",
      "\\midrule\n",
      "estimate       & -0.078825 & -0.071207 & -0.069904 &    -0.074573 \\\\\n",
      "standard error &  0.032509 &  0.032044 &  0.032076 &     0.032104 \\\\\n",
      "\\bottomrule\n",
      "\\end{tabular}\n",
      "\n"
     ]
    }
   ],
   "source": [
    "table2 = np.zeros((2, 4))\n",
    "table2[0,0] = ols_cl.summary2().tables[1]['Coef.']['T2']\n",
    "table2[0,1] = ols_cra.summary2().tables[1]['Coef.']['T2TRUE']\n",
    "table2[0,2] = ols_ira.summary2().tables[1]['Coef.']['T2TRUE']\n",
    "table2[0,3] = rlasso_ira['Coef.']['T2']\n",
    "\n",
    "table2[1,0] = ols_cl.summary2().tables[1]['Std.Err.']['T2']\n",
    "table2[1,1] = ols_cra.summary2().tables[1]['Std.Err.']['T2TRUE']\n",
    "table2[1,2] = ols_ira.summary2().tables[1]['Std.Err.']['T2TRUE']\n",
    "table2[1,3] = rlasso_ira['Std.Err.']['T2']\n",
    "\n",
    "table2 = pd.DataFrame(table2, columns = [\"$CL$\", \"$CRA$\", \"$IRA$\", \"$IRA Lasso$\"], \\\n",
    "                      index = [\"estimate\",\"standard error\"])\n",
    "table2\n",
    "table2.to_latex\n",
    "print(table2.to_latex())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14e87193",
   "metadata": {},
   "source": [
    "Treatment group 2 experiences an average decrease of about $7.1\\%$ in the length of unemployment spell.\n",
    "\n",
    "\n",
    "Observe that classical linear regression adjustment (CRA) delivers estimates that are slighly more efficient (lower standard errors) than the others estimations, but essentially all methods have very similar standard errors.\n",
    "\n",
    "We also see the interactive regression adjustment (IRA) estimators offer slightly lower estimates -- these difference occur perhaps to due minor imbalance in the treatment allocation, which the regression estimators try to correct. However, if we need to choose one model, we would prefer IRA with the Lasso model because this keeps the covariables of interest to the researcher"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3eb0d2ec",
   "metadata": {},
   "source": [
    "#### 2. Using interactive regression adjustment (IRA) plot the coefficients of the next variables\n",
    "- T2*female\n",
    "- T2*black\n",
    "- T2*agelt35\n",
    "- T2*factor(dep)1\n",
    "- Please comment this figure and try to give an explanation for the coefficients."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "a798b8d1",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "ca3cf3ed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td></td>\n",
       "      <td>coef</td>\n",
       "      <td>std err</td>\n",
       "      <td>t</td>\n",
       "      <td>P&gt;|t|</td>\n",
       "      <td>[0.025</td>\n",
       "      <td>0.975]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>(Intercept)</td>\n",
       "      <td>2.0557</td>\n",
       "      <td>0.021</td>\n",
       "      <td>99.243</td>\n",
       "      <td>0.000</td>\n",
       "      <td>2.015</td>\n",
       "      <td>2.096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>T2TRUE</td>\n",
       "      <td>-0.0699</td>\n",
       "      <td>0.032</td>\n",
       "      <td>-2.179</td>\n",
       "      <td>0.029</td>\n",
       "      <td>-0.133</td>\n",
       "      <td>-0.007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Xfemale</td>\n",
       "      <td>-0.6663</td>\n",
       "      <td>0.408</td>\n",
       "      <td>-1.633</td>\n",
       "      <td>0.103</td>\n",
       "      <td>-1.466</td>\n",
       "      <td>0.134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Xblack</td>\n",
       "      <td>-0.8635</td>\n",
       "      <td>0.297</td>\n",
       "      <td>-2.908</td>\n",
       "      <td>0.004</td>\n",
       "      <td>-1.446</td>\n",
       "      <td>-0.281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Xothrace</td>\n",
       "      <td>-3.8177</td>\n",
       "      <td>0.937</td>\n",
       "      <td>-4.076</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-5.654</td>\n",
       "      <td>-1.982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Xfactor(dep)1</td>\n",
       "      <td>0.0359</td>\n",
       "      <td>0.648</td>\n",
       "      <td>0.055</td>\n",
       "      <td>0.956</td>\n",
       "      <td>-1.234</td>\n",
       "      <td>1.306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Xfactor(dep)2</td>\n",
       "      <td>0.2118</td>\n",
       "      <td>0.451</td>\n",
       "      <td>0.469</td>\n",
       "      <td>0.639</td>\n",
       "      <td>-0.673</td>\n",
       "      <td>1.096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Xq2</td>\n",
       "      <td>-0.2546</td>\n",
       "      <td>0.455</td>\n",
       "      <td>-0.559</td>\n",
       "      <td>0.576</td>\n",
       "      <td>-1.147</td>\n",
       "      <td>0.638</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Xq3</td>\n",
       "      <td>-0.6212</td>\n",
       "      <td>0.455</td>\n",
       "      <td>-1.365</td>\n",
       "      <td>0.172</td>\n",
       "      <td>-1.513</td>\n",
       "      <td>0.271</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Xq4</td>\n",
       "      <td>-0.4799</td>\n",
       "      <td>0.456</td>\n",
       "      <td>-1.052</td>\n",
       "      <td>0.293</td>\n",
       "      <td>-1.374</td>\n",
       "      <td>0.414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Xq5</td>\n",
       "      <td>-0.3719</td>\n",
       "      <td>0.454</td>\n",
       "      <td>-0.819</td>\n",
       "      <td>0.413</td>\n",
       "      <td>-1.262</td>\n",
       "      <td>0.518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Xq6</td>\n",
       "      <td>-0.6770</td>\n",
       "      <td>0.452</td>\n",
       "      <td>-1.497</td>\n",
       "      <td>0.134</td>\n",
       "      <td>-1.563</td>\n",
       "      <td>0.209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Xagelt35</td>\n",
       "      <td>-0.6777</td>\n",
       "      <td>0.409</td>\n",
       "      <td>-1.655</td>\n",
       "      <td>0.098</td>\n",
       "      <td>-1.480</td>\n",
       "      <td>0.125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Xagegt54</td>\n",
       "      <td>-0.3041</td>\n",
       "      <td>0.701</td>\n",
       "      <td>-0.434</td>\n",
       "      <td>0.664</td>\n",
       "      <td>-1.678</td>\n",
       "      <td>1.069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Xdurable</td>\n",
       "      <td>-0.8380</td>\n",
       "      <td>0.596</td>\n",
       "      <td>-1.405</td>\n",
       "      <td>0.160</td>\n",
       "      <td>-2.007</td>\n",
       "      <td>0.331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Xlusd</td>\n",
       "      <td>-0.0995</td>\n",
       "      <td>0.228</td>\n",
       "      <td>-0.436</td>\n",
       "      <td>0.662</td>\n",
       "      <td>-0.546</td>\n",
       "      <td>0.347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Xhusd</td>\n",
       "      <td>-0.0626</td>\n",
       "      <td>0.235</td>\n",
       "      <td>-0.267</td>\n",
       "      <td>0.790</td>\n",
       "      <td>-0.523</td>\n",
       "      <td>0.398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Xfemale:black</td>\n",
       "      <td>-0.2155</td>\n",
       "      <td>0.151</td>\n",
       "      <td>-1.432</td>\n",
       "      <td>0.152</td>\n",
       "      <td>-0.511</td>\n",
       "      <td>0.080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Xfemale:othrace</td>\n",
       "      <td>0.5994</td>\n",
       "      <td>0.563</td>\n",
       "      <td>1.066</td>\n",
       "      <td>0.287</td>\n",
       "      <td>-0.503</td>\n",
       "      <td>1.702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Xfemale:factor(dep)1</td>\n",
       "      <td>-0.1734</td>\n",
       "      <td>0.142</td>\n",
       "      <td>-1.222</td>\n",
       "      <td>0.222</td>\n",
       "      <td>-0.452</td>\n",
       "      <td>0.105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Xfemale:factor(dep)2</td>\n",
       "      <td>0.2166</td>\n",
       "      <td>0.126</td>\n",
       "      <td>1.715</td>\n",
       "      <td>0.086</td>\n",
       "      <td>-0.031</td>\n",
       "      <td>0.464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Xfemale:q2</td>\n",
       "      <td>0.3919</td>\n",
       "      <td>0.407</td>\n",
       "      <td>0.964</td>\n",
       "      <td>0.335</td>\n",
       "      <td>-0.405</td>\n",
       "      <td>1.189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Xfemale:q3</td>\n",
       "      <td>0.6850</td>\n",
       "      <td>0.406</td>\n",
       "      <td>1.686</td>\n",
       "      <td>0.092</td>\n",
       "      <td>-0.112</td>\n",
       "      <td>1.482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Xfemale:q4</td>\n",
       "      <td>0.7214</td>\n",
       "      <td>0.406</td>\n",
       "      <td>1.776</td>\n",
       "      <td>0.076</td>\n",
       "      <td>-0.075</td>\n",
       "      <td>1.518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Xfemale:q5</td>\n",
       "      <td>0.5658</td>\n",
       "      <td>0.405</td>\n",
       "      <td>1.398</td>\n",
       "      <td>0.162</td>\n",
       "      <td>-0.228</td>\n",
       "      <td>1.359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Xfemale:q6</td>\n",
       "      <td>0.9082</td>\n",
       "      <td>0.430</td>\n",
       "      <td>2.110</td>\n",
       "      <td>0.035</td>\n",
       "      <td>0.064</td>\n",
       "      <td>1.752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Xfemale:agelt35</td>\n",
       "      <td>0.1701</td>\n",
       "      <td>0.095</td>\n",
       "      <td>1.789</td>\n",
       "      <td>0.074</td>\n",
       "      <td>-0.016</td>\n",
       "      <td>0.356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Xfemale:agegt54</td>\n",
       "      <td>0.2358</td>\n",
       "      <td>0.160</td>\n",
       "      <td>1.472</td>\n",
       "      <td>0.141</td>\n",
       "      <td>-0.078</td>\n",
       "      <td>0.550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Xfemale:durable</td>\n",
       "      <td>0.0974</td>\n",
       "      <td>0.138</td>\n",
       "      <td>0.708</td>\n",
       "      <td>0.479</td>\n",
       "      <td>-0.172</td>\n",
       "      <td>0.367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Xfemale:lusd</td>\n",
       "      <td>0.0720</td>\n",
       "      <td>0.107</td>\n",
       "      <td>0.676</td>\n",
       "      <td>0.499</td>\n",
       "      <td>-0.137</td>\n",
       "      <td>0.281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Xfemale:husd</td>\n",
       "      <td>0.0610</td>\n",
       "      <td>0.116</td>\n",
       "      <td>0.524</td>\n",
       "      <td>0.601</td>\n",
       "      <td>-0.167</td>\n",
       "      <td>0.289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Xblack:factor(dep)1</td>\n",
       "      <td>0.1090</td>\n",
       "      <td>0.228</td>\n",
       "      <td>0.477</td>\n",
       "      <td>0.633</td>\n",
       "      <td>-0.339</td>\n",
       "      <td>0.557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Xblack:factor(dep)2</td>\n",
       "      <td>-0.2007</td>\n",
       "      <td>0.228</td>\n",
       "      <td>-0.878</td>\n",
       "      <td>0.380</td>\n",
       "      <td>-0.649</td>\n",
       "      <td>0.247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>Xblack:q2</td>\n",
       "      <td>0.4267</td>\n",
       "      <td>0.321</td>\n",
       "      <td>1.331</td>\n",
       "      <td>0.183</td>\n",
       "      <td>-0.202</td>\n",
       "      <td>1.055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>Xblack:q3</td>\n",
       "      <td>0.3299</td>\n",
       "      <td>0.303</td>\n",
       "      <td>1.089</td>\n",
       "      <td>0.276</td>\n",
       "      <td>-0.264</td>\n",
       "      <td>0.924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>Xblack:q4</td>\n",
       "      <td>0.5443</td>\n",
       "      <td>0.311</td>\n",
       "      <td>1.753</td>\n",
       "      <td>0.080</td>\n",
       "      <td>-0.064</td>\n",
       "      <td>1.153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>Xblack:q5</td>\n",
       "      <td>0.6597</td>\n",
       "      <td>0.300</td>\n",
       "      <td>2.198</td>\n",
       "      <td>0.028</td>\n",
       "      <td>0.071</td>\n",
       "      <td>1.248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>Xblack:agelt35</td>\n",
       "      <td>0.0830</td>\n",
       "      <td>0.158</td>\n",
       "      <td>0.524</td>\n",
       "      <td>0.600</td>\n",
       "      <td>-0.227</td>\n",
       "      <td>0.393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>Xblack:agegt54</td>\n",
       "      <td>0.4230</td>\n",
       "      <td>0.255</td>\n",
       "      <td>1.660</td>\n",
       "      <td>0.097</td>\n",
       "      <td>-0.076</td>\n",
       "      <td>0.922</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       0           1          2          3       4          5  \\\n",
       "0                               coef    std err          t   P>|t|     [0.025   \n",
       "1            (Intercept)      2.0557      0.021     99.243   0.000      2.015   \n",
       "2                 T2TRUE     -0.0699      0.032     -2.179   0.029     -0.133   \n",
       "3                Xfemale     -0.6663      0.408     -1.633   0.103     -1.466   \n",
       "4                 Xblack     -0.8635      0.297     -2.908   0.004     -1.446   \n",
       "5               Xothrace     -3.8177      0.937     -4.076   0.000     -5.654   \n",
       "6          Xfactor(dep)1      0.0359      0.648      0.055   0.956     -1.234   \n",
       "7          Xfactor(dep)2      0.2118      0.451      0.469   0.639     -0.673   \n",
       "8                    Xq2     -0.2546      0.455     -0.559   0.576     -1.147   \n",
       "9                    Xq3     -0.6212      0.455     -1.365   0.172     -1.513   \n",
       "10                   Xq4     -0.4799      0.456     -1.052   0.293     -1.374   \n",
       "11                   Xq5     -0.3719      0.454     -0.819   0.413     -1.262   \n",
       "12                   Xq6     -0.6770      0.452     -1.497   0.134     -1.563   \n",
       "13              Xagelt35     -0.6777      0.409     -1.655   0.098     -1.480   \n",
       "14              Xagegt54     -0.3041      0.701     -0.434   0.664     -1.678   \n",
       "15              Xdurable     -0.8380      0.596     -1.405   0.160     -2.007   \n",
       "16                 Xlusd     -0.0995      0.228     -0.436   0.662     -0.546   \n",
       "17                 Xhusd     -0.0626      0.235     -0.267   0.790     -0.523   \n",
       "18         Xfemale:black     -0.2155      0.151     -1.432   0.152     -0.511   \n",
       "19       Xfemale:othrace      0.5994      0.563      1.066   0.287     -0.503   \n",
       "20  Xfemale:factor(dep)1     -0.1734      0.142     -1.222   0.222     -0.452   \n",
       "21  Xfemale:factor(dep)2      0.2166      0.126      1.715   0.086     -0.031   \n",
       "22            Xfemale:q2      0.3919      0.407      0.964   0.335     -0.405   \n",
       "23            Xfemale:q3      0.6850      0.406      1.686   0.092     -0.112   \n",
       "24            Xfemale:q4      0.7214      0.406      1.776   0.076     -0.075   \n",
       "25            Xfemale:q5      0.5658      0.405      1.398   0.162     -0.228   \n",
       "26            Xfemale:q6      0.9082      0.430      2.110   0.035      0.064   \n",
       "27       Xfemale:agelt35      0.1701      0.095      1.789   0.074     -0.016   \n",
       "28       Xfemale:agegt54      0.2358      0.160      1.472   0.141     -0.078   \n",
       "29       Xfemale:durable      0.0974      0.138      0.708   0.479     -0.172   \n",
       "30          Xfemale:lusd      0.0720      0.107      0.676   0.499     -0.137   \n",
       "31          Xfemale:husd      0.0610      0.116      0.524   0.601     -0.167   \n",
       "32   Xblack:factor(dep)1      0.1090      0.228      0.477   0.633     -0.339   \n",
       "33   Xblack:factor(dep)2     -0.2007      0.228     -0.878   0.380     -0.649   \n",
       "34             Xblack:q2      0.4267      0.321      1.331   0.183     -0.202   \n",
       "35             Xblack:q3      0.3299      0.303      1.089   0.276     -0.264   \n",
       "36             Xblack:q4      0.5443      0.311      1.753   0.080     -0.064   \n",
       "37             Xblack:q5      0.6597      0.300      2.198   0.028      0.071   \n",
       "38        Xblack:agelt35      0.0830      0.158      0.524   0.600     -0.227   \n",
       "39        Xblack:agegt54      0.4230      0.255      1.660   0.097     -0.076   \n",
       "\n",
       "            6  \n",
       "0      0.975]  \n",
       "1       2.096  \n",
       "2      -0.007  \n",
       "3       0.134  \n",
       "4      -0.281  \n",
       "5      -1.982  \n",
       "6       1.306  \n",
       "7       1.096  \n",
       "8       0.638  \n",
       "9       0.271  \n",
       "10      0.414  \n",
       "11      0.518  \n",
       "12      0.209  \n",
       "13      0.125  \n",
       "14      1.069  \n",
       "15      0.331  \n",
       "16      0.347  \n",
       "17      0.398  \n",
       "18      0.080  \n",
       "19      1.702  \n",
       "20      0.105  \n",
       "21      0.464  \n",
       "22      1.189  \n",
       "23      1.482  \n",
       "24      1.518  \n",
       "25      1.359  \n",
       "26      1.752  \n",
       "27      0.356  \n",
       "28      0.550  \n",
       "29      0.367  \n",
       "30      0.281  \n",
       "31      0.289  \n",
       "32      0.557  \n",
       "33      0.247  \n",
       "34      1.055  \n",
       "35      0.924  \n",
       "36      1.153  \n",
       "37      1.248  \n",
       "38      0.393  \n",
       "39      0.922  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coef_df = pd.DataFrame(ols_ira.summary().tables[1].data)\n",
    "coef_df.head(40)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "1618cdb4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['', 'coef', 'std err', 't', 'P>|t|', '[0.025', '0.975]'], dtype='object', name=0)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coef_df.columns = coef_df.iloc[0]\n",
    "coef_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "9de22f99",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>coef</th>\n",
       "      <th>std err</th>\n",
       "      <th>t</th>\n",
       "      <th>P&gt;|t|</th>\n",
       "      <th>[0.025</th>\n",
       "      <th>0.975]</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>(Intercept)</td>\n",
       "      <td>2.0557</td>\n",
       "      <td>0.021</td>\n",
       "      <td>99.243</td>\n",
       "      <td>0.000</td>\n",
       "      <td>2.015</td>\n",
       "      <td>2.096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>T2TRUE</td>\n",
       "      <td>-0.0699</td>\n",
       "      <td>0.032</td>\n",
       "      <td>-2.179</td>\n",
       "      <td>0.029</td>\n",
       "      <td>-0.133</td>\n",
       "      <td>-0.007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Xfemale</td>\n",
       "      <td>-0.6663</td>\n",
       "      <td>0.408</td>\n",
       "      <td>-1.633</td>\n",
       "      <td>0.103</td>\n",
       "      <td>-1.466</td>\n",
       "      <td>0.134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Xblack</td>\n",
       "      <td>-0.8635</td>\n",
       "      <td>0.297</td>\n",
       "      <td>-2.908</td>\n",
       "      <td>0.004</td>\n",
       "      <td>-1.446</td>\n",
       "      <td>-0.281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Xothrace</td>\n",
       "      <td>-3.8177</td>\n",
       "      <td>0.937</td>\n",
       "      <td>-4.076</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-5.654</td>\n",
       "      <td>-1.982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Xfactor(dep)1</td>\n",
       "      <td>0.0359</td>\n",
       "      <td>0.648</td>\n",
       "      <td>0.055</td>\n",
       "      <td>0.956</td>\n",
       "      <td>-1.234</td>\n",
       "      <td>1.306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Xfactor(dep)2</td>\n",
       "      <td>0.2118</td>\n",
       "      <td>0.451</td>\n",
       "      <td>0.469</td>\n",
       "      <td>0.639</td>\n",
       "      <td>-0.673</td>\n",
       "      <td>1.096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Xq2</td>\n",
       "      <td>-0.2546</td>\n",
       "      <td>0.455</td>\n",
       "      <td>-0.559</td>\n",
       "      <td>0.576</td>\n",
       "      <td>-1.147</td>\n",
       "      <td>0.638</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Xq3</td>\n",
       "      <td>-0.6212</td>\n",
       "      <td>0.455</td>\n",
       "      <td>-1.365</td>\n",
       "      <td>0.172</td>\n",
       "      <td>-1.513</td>\n",
       "      <td>0.271</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Xq4</td>\n",
       "      <td>-0.4799</td>\n",
       "      <td>0.456</td>\n",
       "      <td>-1.052</td>\n",
       "      <td>0.293</td>\n",
       "      <td>-1.374</td>\n",
       "      <td>0.414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Xq5</td>\n",
       "      <td>-0.3719</td>\n",
       "      <td>0.454</td>\n",
       "      <td>-0.819</td>\n",
       "      <td>0.413</td>\n",
       "      <td>-1.262</td>\n",
       "      <td>0.518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Xq6</td>\n",
       "      <td>-0.6770</td>\n",
       "      <td>0.452</td>\n",
       "      <td>-1.497</td>\n",
       "      <td>0.134</td>\n",
       "      <td>-1.563</td>\n",
       "      <td>0.209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Xagelt35</td>\n",
       "      <td>-0.6777</td>\n",
       "      <td>0.409</td>\n",
       "      <td>-1.655</td>\n",
       "      <td>0.098</td>\n",
       "      <td>-1.480</td>\n",
       "      <td>0.125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Xagegt54</td>\n",
       "      <td>-0.3041</td>\n",
       "      <td>0.701</td>\n",
       "      <td>-0.434</td>\n",
       "      <td>0.664</td>\n",
       "      <td>-1.678</td>\n",
       "      <td>1.069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Xdurable</td>\n",
       "      <td>-0.8380</td>\n",
       "      <td>0.596</td>\n",
       "      <td>-1.405</td>\n",
       "      <td>0.160</td>\n",
       "      <td>-2.007</td>\n",
       "      <td>0.331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Xlusd</td>\n",
       "      <td>-0.0995</td>\n",
       "      <td>0.228</td>\n",
       "      <td>-0.436</td>\n",
       "      <td>0.662</td>\n",
       "      <td>-0.546</td>\n",
       "      <td>0.347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Xhusd</td>\n",
       "      <td>-0.0626</td>\n",
       "      <td>0.235</td>\n",
       "      <td>-0.267</td>\n",
       "      <td>0.790</td>\n",
       "      <td>-0.523</td>\n",
       "      <td>0.398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Xfemale:black</td>\n",
       "      <td>-0.2155</td>\n",
       "      <td>0.151</td>\n",
       "      <td>-1.432</td>\n",
       "      <td>0.152</td>\n",
       "      <td>-0.511</td>\n",
       "      <td>0.080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Xfemale:othrace</td>\n",
       "      <td>0.5994</td>\n",
       "      <td>0.563</td>\n",
       "      <td>1.066</td>\n",
       "      <td>0.287</td>\n",
       "      <td>-0.503</td>\n",
       "      <td>1.702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Xfemale:factor(dep)1</td>\n",
       "      <td>-0.1734</td>\n",
       "      <td>0.142</td>\n",
       "      <td>-1.222</td>\n",
       "      <td>0.222</td>\n",
       "      <td>-0.452</td>\n",
       "      <td>0.105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Xfemale:factor(dep)2</td>\n",
       "      <td>0.2166</td>\n",
       "      <td>0.126</td>\n",
       "      <td>1.715</td>\n",
       "      <td>0.086</td>\n",
       "      <td>-0.031</td>\n",
       "      <td>0.464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Xfemale:q2</td>\n",
       "      <td>0.3919</td>\n",
       "      <td>0.407</td>\n",
       "      <td>0.964</td>\n",
       "      <td>0.335</td>\n",
       "      <td>-0.405</td>\n",
       "      <td>1.189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Xfemale:q3</td>\n",
       "      <td>0.6850</td>\n",
       "      <td>0.406</td>\n",
       "      <td>1.686</td>\n",
       "      <td>0.092</td>\n",
       "      <td>-0.112</td>\n",
       "      <td>1.482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Xfemale:q4</td>\n",
       "      <td>0.7214</td>\n",
       "      <td>0.406</td>\n",
       "      <td>1.776</td>\n",
       "      <td>0.076</td>\n",
       "      <td>-0.075</td>\n",
       "      <td>1.518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Xfemale:q5</td>\n",
       "      <td>0.5658</td>\n",
       "      <td>0.405</td>\n",
       "      <td>1.398</td>\n",
       "      <td>0.162</td>\n",
       "      <td>-0.228</td>\n",
       "      <td>1.359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Xfemale:q6</td>\n",
       "      <td>0.9082</td>\n",
       "      <td>0.430</td>\n",
       "      <td>2.110</td>\n",
       "      <td>0.035</td>\n",
       "      <td>0.064</td>\n",
       "      <td>1.752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Xfemale:agelt35</td>\n",
       "      <td>0.1701</td>\n",
       "      <td>0.095</td>\n",
       "      <td>1.789</td>\n",
       "      <td>0.074</td>\n",
       "      <td>-0.016</td>\n",
       "      <td>0.356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Xfemale:agegt54</td>\n",
       "      <td>0.2358</td>\n",
       "      <td>0.160</td>\n",
       "      <td>1.472</td>\n",
       "      <td>0.141</td>\n",
       "      <td>-0.078</td>\n",
       "      <td>0.550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Xfemale:durable</td>\n",
       "      <td>0.0974</td>\n",
       "      <td>0.138</td>\n",
       "      <td>0.708</td>\n",
       "      <td>0.479</td>\n",
       "      <td>-0.172</td>\n",
       "      <td>0.367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Xfemale:lusd</td>\n",
       "      <td>0.0720</td>\n",
       "      <td>0.107</td>\n",
       "      <td>0.676</td>\n",
       "      <td>0.499</td>\n",
       "      <td>-0.137</td>\n",
       "      <td>0.281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Xfemale:husd</td>\n",
       "      <td>0.0610</td>\n",
       "      <td>0.116</td>\n",
       "      <td>0.524</td>\n",
       "      <td>0.601</td>\n",
       "      <td>-0.167</td>\n",
       "      <td>0.289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Xblack:factor(dep)1</td>\n",
       "      <td>0.1090</td>\n",
       "      <td>0.228</td>\n",
       "      <td>0.477</td>\n",
       "      <td>0.633</td>\n",
       "      <td>-0.339</td>\n",
       "      <td>0.557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Xblack:factor(dep)2</td>\n",
       "      <td>-0.2007</td>\n",
       "      <td>0.228</td>\n",
       "      <td>-0.878</td>\n",
       "      <td>0.380</td>\n",
       "      <td>-0.649</td>\n",
       "      <td>0.247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>Xblack:q2</td>\n",
       "      <td>0.4267</td>\n",
       "      <td>0.321</td>\n",
       "      <td>1.331</td>\n",
       "      <td>0.183</td>\n",
       "      <td>-0.202</td>\n",
       "      <td>1.055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>Xblack:q3</td>\n",
       "      <td>0.3299</td>\n",
       "      <td>0.303</td>\n",
       "      <td>1.089</td>\n",
       "      <td>0.276</td>\n",
       "      <td>-0.264</td>\n",
       "      <td>0.924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>Xblack:q4</td>\n",
       "      <td>0.5443</td>\n",
       "      <td>0.311</td>\n",
       "      <td>1.753</td>\n",
       "      <td>0.080</td>\n",
       "      <td>-0.064</td>\n",
       "      <td>1.153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>Xblack:q5</td>\n",
       "      <td>0.6597</td>\n",
       "      <td>0.300</td>\n",
       "      <td>2.198</td>\n",
       "      <td>0.028</td>\n",
       "      <td>0.071</td>\n",
       "      <td>1.248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>Xblack:agelt35</td>\n",
       "      <td>0.0830</td>\n",
       "      <td>0.158</td>\n",
       "      <td>0.524</td>\n",
       "      <td>0.600</td>\n",
       "      <td>-0.227</td>\n",
       "      <td>0.393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>Xblack:agegt54</td>\n",
       "      <td>0.4230</td>\n",
       "      <td>0.255</td>\n",
       "      <td>1.660</td>\n",
       "      <td>0.097</td>\n",
       "      <td>-0.076</td>\n",
       "      <td>0.922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>Xblack:durable</td>\n",
       "      <td>0.4681</td>\n",
       "      <td>0.227</td>\n",
       "      <td>2.058</td>\n",
       "      <td>0.040</td>\n",
       "      <td>0.022</td>\n",
       "      <td>0.914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>Xblack:lusd</td>\n",
       "      <td>0.3086</td>\n",
       "      <td>0.184</td>\n",
       "      <td>1.674</td>\n",
       "      <td>0.094</td>\n",
       "      <td>-0.053</td>\n",
       "      <td>0.670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>Xblack:husd</td>\n",
       "      <td>0.5498</td>\n",
       "      <td>0.660</td>\n",
       "      <td>0.834</td>\n",
       "      <td>0.405</td>\n",
       "      <td>-0.743</td>\n",
       "      <td>1.843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>Xothrace:factor(dep)1</td>\n",
       "      <td>2.5202</td>\n",
       "      <td>1.855</td>\n",
       "      <td>1.359</td>\n",
       "      <td>0.174</td>\n",
       "      <td>-1.116</td>\n",
       "      <td>6.157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>Xothrace:factor(dep)2</td>\n",
       "      <td>1.1889</td>\n",
       "      <td>0.573</td>\n",
       "      <td>2.074</td>\n",
       "      <td>0.038</td>\n",
       "      <td>0.065</td>\n",
       "      <td>2.312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>Xothrace:q2</td>\n",
       "      <td>2.0826</td>\n",
       "      <td>0.608</td>\n",
       "      <td>3.423</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.890</td>\n",
       "      <td>3.275</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "0                                coef    std err          t   P>|t|  \\\n",
       "1             (Intercept)      2.0557      0.021     99.243   0.000   \n",
       "2                  T2TRUE     -0.0699      0.032     -2.179   0.029   \n",
       "3                 Xfemale     -0.6663      0.408     -1.633   0.103   \n",
       "4                  Xblack     -0.8635      0.297     -2.908   0.004   \n",
       "5                Xothrace     -3.8177      0.937     -4.076   0.000   \n",
       "6           Xfactor(dep)1      0.0359      0.648      0.055   0.956   \n",
       "7           Xfactor(dep)2      0.2118      0.451      0.469   0.639   \n",
       "8                     Xq2     -0.2546      0.455     -0.559   0.576   \n",
       "9                     Xq3     -0.6212      0.455     -1.365   0.172   \n",
       "10                    Xq4     -0.4799      0.456     -1.052   0.293   \n",
       "11                    Xq5     -0.3719      0.454     -0.819   0.413   \n",
       "12                    Xq6     -0.6770      0.452     -1.497   0.134   \n",
       "13               Xagelt35     -0.6777      0.409     -1.655   0.098   \n",
       "14               Xagegt54     -0.3041      0.701     -0.434   0.664   \n",
       "15               Xdurable     -0.8380      0.596     -1.405   0.160   \n",
       "16                  Xlusd     -0.0995      0.228     -0.436   0.662   \n",
       "17                  Xhusd     -0.0626      0.235     -0.267   0.790   \n",
       "18          Xfemale:black     -0.2155      0.151     -1.432   0.152   \n",
       "19        Xfemale:othrace      0.5994      0.563      1.066   0.287   \n",
       "20   Xfemale:factor(dep)1     -0.1734      0.142     -1.222   0.222   \n",
       "21   Xfemale:factor(dep)2      0.2166      0.126      1.715   0.086   \n",
       "22             Xfemale:q2      0.3919      0.407      0.964   0.335   \n",
       "23             Xfemale:q3      0.6850      0.406      1.686   0.092   \n",
       "24             Xfemale:q4      0.7214      0.406      1.776   0.076   \n",
       "25             Xfemale:q5      0.5658      0.405      1.398   0.162   \n",
       "26             Xfemale:q6      0.9082      0.430      2.110   0.035   \n",
       "27        Xfemale:agelt35      0.1701      0.095      1.789   0.074   \n",
       "28        Xfemale:agegt54      0.2358      0.160      1.472   0.141   \n",
       "29        Xfemale:durable      0.0974      0.138      0.708   0.479   \n",
       "30           Xfemale:lusd      0.0720      0.107      0.676   0.499   \n",
       "31           Xfemale:husd      0.0610      0.116      0.524   0.601   \n",
       "32    Xblack:factor(dep)1      0.1090      0.228      0.477   0.633   \n",
       "33    Xblack:factor(dep)2     -0.2007      0.228     -0.878   0.380   \n",
       "34              Xblack:q2      0.4267      0.321      1.331   0.183   \n",
       "35              Xblack:q3      0.3299      0.303      1.089   0.276   \n",
       "36              Xblack:q4      0.5443      0.311      1.753   0.080   \n",
       "37              Xblack:q5      0.6597      0.300      2.198   0.028   \n",
       "38         Xblack:agelt35      0.0830      0.158      0.524   0.600   \n",
       "39         Xblack:agegt54      0.4230      0.255      1.660   0.097   \n",
       "40         Xblack:durable      0.4681      0.227      2.058   0.040   \n",
       "41            Xblack:lusd      0.3086      0.184      1.674   0.094   \n",
       "42            Xblack:husd      0.5498      0.660      0.834   0.405   \n",
       "43  Xothrace:factor(dep)1      2.5202      1.855      1.359   0.174   \n",
       "44  Xothrace:factor(dep)2      1.1889      0.573      2.074   0.038   \n",
       "45            Xothrace:q2      2.0826      0.608      3.423   0.001   \n",
       "\n",
       "0      [0.025     0.975]  \n",
       "1       2.015      2.096  \n",
       "2      -0.133     -0.007  \n",
       "3      -1.466      0.134  \n",
       "4      -1.446     -0.281  \n",
       "5      -5.654     -1.982  \n",
       "6      -1.234      1.306  \n",
       "7      -0.673      1.096  \n",
       "8      -1.147      0.638  \n",
       "9      -1.513      0.271  \n",
       "10     -1.374      0.414  \n",
       "11     -1.262      0.518  \n",
       "12     -1.563      0.209  \n",
       "13     -1.480      0.125  \n",
       "14     -1.678      1.069  \n",
       "15     -2.007      0.331  \n",
       "16     -0.546      0.347  \n",
       "17     -0.523      0.398  \n",
       "18     -0.511      0.080  \n",
       "19     -0.503      1.702  \n",
       "20     -0.452      0.105  \n",
       "21     -0.031      0.464  \n",
       "22     -0.405      1.189  \n",
       "23     -0.112      1.482  \n",
       "24     -0.075      1.518  \n",
       "25     -0.228      1.359  \n",
       "26      0.064      1.752  \n",
       "27     -0.016      0.356  \n",
       "28     -0.078      0.550  \n",
       "29     -0.172      0.367  \n",
       "30     -0.137      0.281  \n",
       "31     -0.167      0.289  \n",
       "32     -0.339      0.557  \n",
       "33     -0.649      0.247  \n",
       "34     -0.202      1.055  \n",
       "35     -0.264      0.924  \n",
       "36     -0.064      1.153  \n",
       "37      0.071      1.248  \n",
       "38     -0.227      0.393  \n",
       "39     -0.076      0.922  \n",
       "40      0.022      0.914  \n",
       "41     -0.053      0.670  \n",
       "42     -0.743      1.843  \n",
       "43     -1.116      6.157  \n",
       "44      0.065      2.312  \n",
       "45      0.890      3.275  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coef_df=coef_df.drop(0)\n",
    "coef_df.head(45)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "60e5a0b6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>coef</th>\n",
       "      <th>std err</th>\n",
       "      <th>t</th>\n",
       "      <th>P&gt;|t|</th>\n",
       "      <th>[0.025</th>\n",
       "      <th>0.975]</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>(Intercept)</th>\n",
       "      <td>2.0557</td>\n",
       "      <td>0.021</td>\n",
       "      <td>99.243</td>\n",
       "      <td>0.000</td>\n",
       "      <td>2.015</td>\n",
       "      <td>2.096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T2TRUE</th>\n",
       "      <td>-0.0699</td>\n",
       "      <td>0.032</td>\n",
       "      <td>-2.179</td>\n",
       "      <td>0.029</td>\n",
       "      <td>-0.133</td>\n",
       "      <td>-0.007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Xfemale</th>\n",
       "      <td>-0.6663</td>\n",
       "      <td>0.408</td>\n",
       "      <td>-1.633</td>\n",
       "      <td>0.103</td>\n",
       "      <td>-1.466</td>\n",
       "      <td>0.134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Xblack</th>\n",
       "      <td>-0.8635</td>\n",
       "      <td>0.297</td>\n",
       "      <td>-2.908</td>\n",
       "      <td>0.004</td>\n",
       "      <td>-1.446</td>\n",
       "      <td>-0.281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Xothrace</th>\n",
       "      <td>-3.8177</td>\n",
       "      <td>0.937</td>\n",
       "      <td>-4.076</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-5.654</td>\n",
       "      <td>-1.982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T2TRUE:Xagegt54:durable</th>\n",
       "      <td>0.1732</td>\n",
       "      <td>0.318</td>\n",
       "      <td>0.545</td>\n",
       "      <td>0.586</td>\n",
       "      <td>-0.450</td>\n",
       "      <td>0.796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T2TRUE:Xagegt54:lusd</th>\n",
       "      <td>-0.1245</td>\n",
       "      <td>0.267</td>\n",
       "      <td>-0.467</td>\n",
       "      <td>0.641</td>\n",
       "      <td>-0.647</td>\n",
       "      <td>0.398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T2TRUE:Xagegt54:husd</th>\n",
       "      <td>0.2899</td>\n",
       "      <td>0.296</td>\n",
       "      <td>0.979</td>\n",
       "      <td>0.328</td>\n",
       "      <td>-0.291</td>\n",
       "      <td>0.870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T2TRUE:Xdurable:lusd</th>\n",
       "      <td>0.1281</td>\n",
       "      <td>0.212</td>\n",
       "      <td>0.605</td>\n",
       "      <td>0.545</td>\n",
       "      <td>-0.287</td>\n",
       "      <td>0.543</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T2TRUE:Xdurable:husd</th>\n",
       "      <td>0.0133</td>\n",
       "      <td>0.255</td>\n",
       "      <td>0.052</td>\n",
       "      <td>0.958</td>\n",
       "      <td>-0.487</td>\n",
       "      <td>0.514</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>203 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "0                              coef    std err          t   P>|t|     [0.025  \\\n",
       "                                                                               \n",
       "(Intercept)                  2.0557      0.021     99.243   0.000      2.015   \n",
       "T2TRUE                      -0.0699      0.032     -2.179   0.029     -0.133   \n",
       "Xfemale                     -0.6663      0.408     -1.633   0.103     -1.466   \n",
       "Xblack                      -0.8635      0.297     -2.908   0.004     -1.446   \n",
       "Xothrace                    -3.8177      0.937     -4.076   0.000     -5.654   \n",
       "...                             ...        ...        ...     ...        ...   \n",
       "T2TRUE:Xagegt54:durable      0.1732      0.318      0.545   0.586     -0.450   \n",
       "T2TRUE:Xagegt54:lusd        -0.1245      0.267     -0.467   0.641     -0.647   \n",
       "T2TRUE:Xagegt54:husd         0.2899      0.296      0.979   0.328     -0.291   \n",
       "T2TRUE:Xdurable:lusd         0.1281      0.212      0.605   0.545     -0.287   \n",
       "T2TRUE:Xdurable:husd         0.0133      0.255      0.052   0.958     -0.487   \n",
       "\n",
       "0                           0.975]  \n",
       "                                    \n",
       "(Intercept)                  2.096  \n",
       "T2TRUE                      -0.007  \n",
       "Xfemale                      0.134  \n",
       "Xblack                      -0.281  \n",
       "Xothrace                    -1.982  \n",
       "...                            ...  \n",
       "T2TRUE:Xagegt54:durable      0.796  \n",
       "T2TRUE:Xagegt54:lusd         0.398  \n",
       "T2TRUE:Xagegt54:husd         0.870  \n",
       "T2TRUE:Xdurable:lusd         0.543  \n",
       "T2TRUE:Xdurable:husd         0.514  \n",
       "\n",
       "[203 rows x 6 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coef_df = coef_df.set_index(coef_df.columns[0])\n",
    "coef_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "0d8ef3ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "coef_df = coef_df.astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "56b729e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['(Intercept)',\n",
       " 'T2TRUE',\n",
       " 'Xfemale',\n",
       " 'Xblack',\n",
       " 'Xothrace',\n",
       " 'Xfactor(dep)1',\n",
       " 'Xfactor(dep)2',\n",
       " 'Xq2',\n",
       " 'Xq3',\n",
       " 'Xq4',\n",
       " 'Xq5',\n",
       " 'Xq6',\n",
       " 'Xagelt35',\n",
       " 'Xagegt54',\n",
       " 'Xdurable',\n",
       " 'Xlusd',\n",
       " 'Xhusd',\n",
       " 'Xfemale:black',\n",
       " 'Xfemale:othrace',\n",
       " 'Xfemale:factor(dep)1',\n",
       " 'Xfemale:factor(dep)2',\n",
       " 'Xfemale:q2',\n",
       " 'Xfemale:q3',\n",
       " 'Xfemale:q4',\n",
       " 'Xfemale:q5',\n",
       " 'Xfemale:q6',\n",
       " 'Xfemale:agelt35',\n",
       " 'Xfemale:agegt54',\n",
       " 'Xfemale:durable',\n",
       " 'Xfemale:lusd',\n",
       " 'Xfemale:husd',\n",
       " 'Xblack:factor(dep)1',\n",
       " 'Xblack:factor(dep)2',\n",
       " 'Xblack:q2',\n",
       " 'Xblack:q3',\n",
       " 'Xblack:q4',\n",
       " 'Xblack:q5',\n",
       " 'Xblack:agelt35',\n",
       " 'Xblack:agegt54',\n",
       " 'Xblack:durable',\n",
       " 'Xblack:lusd',\n",
       " 'Xblack:husd',\n",
       " 'Xothrace:factor(dep)1',\n",
       " 'Xothrace:factor(dep)2',\n",
       " 'Xothrace:q2',\n",
       " 'Xothrace:q3',\n",
       " 'Xothrace:q4',\n",
       " 'Xothrace:q5',\n",
       " 'Xothrace:agelt35',\n",
       " 'Xothrace:agegt54',\n",
       " 'Xothrace:durable',\n",
       " 'Xothrace:lusd',\n",
       " 'Xothrace:husd',\n",
       " 'Xfactor(dep)1:q2',\n",
       " 'Xfactor(dep)2:q2',\n",
       " 'Xfactor(dep)1:q3',\n",
       " 'Xfactor(dep)2:q3',\n",
       " 'Xfactor(dep)1:q4',\n",
       " 'Xfactor(dep)2:q4',\n",
       " 'Xfactor(dep)1:q5',\n",
       " 'Xfactor(dep)2:q5',\n",
       " 'Xfactor(dep)1:q6',\n",
       " 'Xfactor(dep)2:q6',\n",
       " 'Xfactor(dep)1:agelt35',\n",
       " 'Xfactor(dep)2:agelt35',\n",
       " 'Xfactor(dep)1:agegt54',\n",
       " 'Xfactor(dep)2:agegt54',\n",
       " 'Xfactor(dep)1:durable',\n",
       " 'Xfactor(dep)2:durable',\n",
       " 'Xfactor(dep)1:lusd',\n",
       " 'Xfactor(dep)2:lusd',\n",
       " 'Xfactor(dep)1:husd',\n",
       " 'Xfactor(dep)2:husd',\n",
       " 'Xq2:agelt35',\n",
       " 'Xq2:agegt54',\n",
       " 'Xq2:durable',\n",
       " 'Xq2:lusd',\n",
       " 'Xq2:husd',\n",
       " 'Xq3:agelt35',\n",
       " 'Xq3:agegt54',\n",
       " 'Xq3:durable',\n",
       " 'Xq3:lusd',\n",
       " 'Xq3:husd',\n",
       " 'Xq4:agelt35',\n",
       " 'Xq4:agegt54',\n",
       " 'Xq4:durable',\n",
       " 'Xq4:lusd',\n",
       " 'Xq4:husd',\n",
       " 'Xq5:agelt35',\n",
       " 'Xq5:agegt54',\n",
       " 'Xq5:durable',\n",
       " 'Xq5:lusd',\n",
       " 'Xq5:husd',\n",
       " 'Xq6:agelt35',\n",
       " 'Xq6:agegt54',\n",
       " 'Xq6:durable',\n",
       " 'Xagelt35:durable',\n",
       " 'Xagelt35:lusd',\n",
       " 'Xagelt35:husd',\n",
       " 'Xagegt54:durable',\n",
       " 'Xagegt54:lusd',\n",
       " 'Xagegt54:husd',\n",
       " 'Xdurable:lusd',\n",
       " 'Xdurable:husd',\n",
       " 'T2TRUE:Xfemale',\n",
       " 'T2TRUE:Xblack',\n",
       " 'T2TRUE:Xothrace',\n",
       " 'T2TRUE:Xfactor(dep)1',\n",
       " 'T2TRUE:Xfactor(dep)2',\n",
       " 'T2TRUE:Xq2',\n",
       " 'T2TRUE:Xq3',\n",
       " 'T2TRUE:Xq4',\n",
       " 'T2TRUE:Xq5',\n",
       " 'T2TRUE:Xq6',\n",
       " 'T2TRUE:Xagelt35',\n",
       " 'T2TRUE:Xagegt54',\n",
       " 'T2TRUE:Xdurable',\n",
       " 'T2TRUE:Xlusd',\n",
       " 'T2TRUE:Xhusd',\n",
       " 'T2TRUE:Xfemale:black',\n",
       " 'T2TRUE:Xfemale:othrace',\n",
       " 'T2TRUE:Xfemale:factor(dep)1',\n",
       " 'T2TRUE:Xfemale:factor(dep)2',\n",
       " 'T2TRUE:Xfemale:q2',\n",
       " 'T2TRUE:Xfemale:q3',\n",
       " 'T2TRUE:Xfemale:q4',\n",
       " 'T2TRUE:Xfemale:q5',\n",
       " 'T2TRUE:Xfemale:q6',\n",
       " 'T2TRUE:Xfemale:agelt35',\n",
       " 'T2TRUE:Xfemale:agegt54',\n",
       " 'T2TRUE:Xfemale:durable',\n",
       " 'T2TRUE:Xfemale:lusd',\n",
       " 'T2TRUE:Xfemale:husd',\n",
       " 'T2TRUE:Xblack:factor(dep)1',\n",
       " 'T2TRUE:Xblack:factor(dep)2',\n",
       " 'T2TRUE:Xblack:q2',\n",
       " 'T2TRUE:Xblack:q3',\n",
       " 'T2TRUE:Xblack:q4',\n",
       " 'T2TRUE:Xblack:q5',\n",
       " 'T2TRUE:Xblack:agelt35',\n",
       " 'T2TRUE:Xblack:agegt54',\n",
       " 'T2TRUE:Xblack:durable',\n",
       " 'T2TRUE:Xblack:lusd',\n",
       " 'T2TRUE:Xblack:husd',\n",
       " 'T2TRUE:Xothrace:factor(dep)1',\n",
       " 'T2TRUE:Xothrace:factor(dep)2',\n",
       " 'T2TRUE:Xothrace:q3',\n",
       " 'T2TRUE:Xothrace:q4',\n",
       " 'T2TRUE:Xothrace:q5',\n",
       " 'T2TRUE:Xothrace:agelt35',\n",
       " 'T2TRUE:Xothrace:durable',\n",
       " 'T2TRUE:Xothrace:lusd',\n",
       " 'T2TRUE:Xfactor(dep)1:q2',\n",
       " 'T2TRUE:Xfactor(dep)2:q2',\n",
       " 'T2TRUE:Xfactor(dep)1:q3',\n",
       " 'T2TRUE:Xfactor(dep)2:q3',\n",
       " 'T2TRUE:Xfactor(dep)1:q4',\n",
       " 'T2TRUE:Xfactor(dep)2:q4',\n",
       " 'T2TRUE:Xfactor(dep)1:q5',\n",
       " 'T2TRUE:Xfactor(dep)2:q5',\n",
       " 'T2TRUE:Xfactor(dep)1:q6',\n",
       " 'T2TRUE:Xfactor(dep)2:q6',\n",
       " 'T2TRUE:Xfactor(dep)1:agelt35',\n",
       " 'T2TRUE:Xfactor(dep)2:agelt35',\n",
       " 'T2TRUE:Xfactor(dep)1:agegt54',\n",
       " 'T2TRUE:Xfactor(dep)2:agegt54',\n",
       " 'T2TRUE:Xfactor(dep)1:durable',\n",
       " 'T2TRUE:Xfactor(dep)2:durable',\n",
       " 'T2TRUE:Xfactor(dep)1:lusd',\n",
       " 'T2TRUE:Xfactor(dep)2:lusd',\n",
       " 'T2TRUE:Xfactor(dep)1:husd',\n",
       " 'T2TRUE:Xfactor(dep)2:husd',\n",
       " 'T2TRUE:Xq2:agelt35',\n",
       " 'T2TRUE:Xq2:agegt54',\n",
       " 'T2TRUE:Xq2:durable',\n",
       " 'T2TRUE:Xq2:lusd',\n",
       " 'T2TRUE:Xq2:husd',\n",
       " 'T2TRUE:Xq3:agelt35',\n",
       " 'T2TRUE:Xq3:agegt54',\n",
       " 'T2TRUE:Xq3:durable',\n",
       " 'T2TRUE:Xq3:lusd',\n",
       " 'T2TRUE:Xq3:husd',\n",
       " 'T2TRUE:Xq4:agelt35',\n",
       " 'T2TRUE:Xq4:agegt54',\n",
       " 'T2TRUE:Xq4:durable',\n",
       " 'T2TRUE:Xq4:lusd',\n",
       " 'T2TRUE:Xq4:husd',\n",
       " 'T2TRUE:Xq5:agelt35',\n",
       " 'T2TRUE:Xq5:agegt54',\n",
       " 'T2TRUE:Xq5:durable',\n",
       " 'T2TRUE:Xq5:lusd',\n",
       " 'T2TRUE:Xq5:husd',\n",
       " 'T2TRUE:Xq6:agelt35',\n",
       " 'T2TRUE:Xq6:agegt54',\n",
       " 'T2TRUE:Xq6:durable',\n",
       " 'T2TRUE:Xagelt35:durable',\n",
       " 'T2TRUE:Xagelt35:lusd',\n",
       " 'T2TRUE:Xagelt35:husd',\n",
       " 'T2TRUE:Xagegt54:durable',\n",
       " 'T2TRUE:Xagegt54:lusd',\n",
       " 'T2TRUE:Xagegt54:husd',\n",
       " 'T2TRUE:Xdurable:lusd',\n",
       " 'T2TRUE:Xdurable:husd']"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "variables = list(coef_df.index.values)\n",
    "variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "9c3112fc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>coef</th>\n",
       "      <th>std err</th>\n",
       "      <th>t</th>\n",
       "      <th>P&gt;|t|</th>\n",
       "      <th>[0.025</th>\n",
       "      <th>0.975]</th>\n",
       "      <th>variables</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>(Intercept)</th>\n",
       "      <td>2.0557</td>\n",
       "      <td>0.021</td>\n",
       "      <td>99.243</td>\n",
       "      <td>0.000</td>\n",
       "      <td>2.015</td>\n",
       "      <td>2.096</td>\n",
       "      <td>(Intercept)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T2TRUE</th>\n",
       "      <td>-0.0699</td>\n",
       "      <td>0.032</td>\n",
       "      <td>-2.179</td>\n",
       "      <td>0.029</td>\n",
       "      <td>-0.133</td>\n",
       "      <td>-0.007</td>\n",
       "      <td>T2TRUE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Xfemale</th>\n",
       "      <td>-0.6663</td>\n",
       "      <td>0.408</td>\n",
       "      <td>-1.633</td>\n",
       "      <td>0.103</td>\n",
       "      <td>-1.466</td>\n",
       "      <td>0.134</td>\n",
       "      <td>Xfemale</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Xblack</th>\n",
       "      <td>-0.8635</td>\n",
       "      <td>0.297</td>\n",
       "      <td>-2.908</td>\n",
       "      <td>0.004</td>\n",
       "      <td>-1.446</td>\n",
       "      <td>-0.281</td>\n",
       "      <td>Xblack</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Xothrace</th>\n",
       "      <td>-3.8177</td>\n",
       "      <td>0.937</td>\n",
       "      <td>-4.076</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-5.654</td>\n",
       "      <td>-1.982</td>\n",
       "      <td>Xothrace</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T2TRUE:Xagegt54:durable</th>\n",
       "      <td>0.1732</td>\n",
       "      <td>0.318</td>\n",
       "      <td>0.545</td>\n",
       "      <td>0.586</td>\n",
       "      <td>-0.450</td>\n",
       "      <td>0.796</td>\n",
       "      <td>T2TRUE:Xagegt54:durable</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T2TRUE:Xagegt54:lusd</th>\n",
       "      <td>-0.1245</td>\n",
       "      <td>0.267</td>\n",
       "      <td>-0.467</td>\n",
       "      <td>0.641</td>\n",
       "      <td>-0.647</td>\n",
       "      <td>0.398</td>\n",
       "      <td>T2TRUE:Xagegt54:lusd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T2TRUE:Xagegt54:husd</th>\n",
       "      <td>0.2899</td>\n",
       "      <td>0.296</td>\n",
       "      <td>0.979</td>\n",
       "      <td>0.328</td>\n",
       "      <td>-0.291</td>\n",
       "      <td>0.870</td>\n",
       "      <td>T2TRUE:Xagegt54:husd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T2TRUE:Xdurable:lusd</th>\n",
       "      <td>0.1281</td>\n",
       "      <td>0.212</td>\n",
       "      <td>0.605</td>\n",
       "      <td>0.545</td>\n",
       "      <td>-0.287</td>\n",
       "      <td>0.543</td>\n",
       "      <td>T2TRUE:Xdurable:lusd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T2TRUE:Xdurable:husd</th>\n",
       "      <td>0.0133</td>\n",
       "      <td>0.255</td>\n",
       "      <td>0.052</td>\n",
       "      <td>0.958</td>\n",
       "      <td>-0.487</td>\n",
       "      <td>0.514</td>\n",
       "      <td>T2TRUE:Xdurable:husd</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>203 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "0                          coef  std err       t  P>|t|  [0.025  0.975]  \\\n",
       "                                                                          \n",
       "(Intercept)              2.0557    0.021  99.243  0.000   2.015   2.096   \n",
       "T2TRUE                  -0.0699    0.032  -2.179  0.029  -0.133  -0.007   \n",
       "Xfemale                 -0.6663    0.408  -1.633  0.103  -1.466   0.134   \n",
       "Xblack                  -0.8635    0.297  -2.908  0.004  -1.446  -0.281   \n",
       "Xothrace                -3.8177    0.937  -4.076  0.000  -5.654  -1.982   \n",
       "...                         ...      ...     ...    ...     ...     ...   \n",
       "T2TRUE:Xagegt54:durable  0.1732    0.318   0.545  0.586  -0.450   0.796   \n",
       "T2TRUE:Xagegt54:lusd    -0.1245    0.267  -0.467  0.641  -0.647   0.398   \n",
       "T2TRUE:Xagegt54:husd     0.2899    0.296   0.979  0.328  -0.291   0.870   \n",
       "T2TRUE:Xdurable:lusd     0.1281    0.212   0.605  0.545  -0.287   0.543   \n",
       "T2TRUE:Xdurable:husd     0.0133    0.255   0.052  0.958  -0.487   0.514   \n",
       "\n",
       "0                                      variables  \n",
       "                                                  \n",
       "(Intercept)                          (Intercept)  \n",
       "T2TRUE                                    T2TRUE  \n",
       "Xfemale                                  Xfemale  \n",
       "Xblack                                    Xblack  \n",
       "Xothrace                                Xothrace  \n",
       "...                                          ...  \n",
       "T2TRUE:Xagegt54:durable  T2TRUE:Xagegt54:durable  \n",
       "T2TRUE:Xagegt54:lusd        T2TRUE:Xagegt54:lusd  \n",
       "T2TRUE:Xagegt54:husd        T2TRUE:Xagegt54:husd  \n",
       "T2TRUE:Xdurable:lusd        T2TRUE:Xdurable:lusd  \n",
       "T2TRUE:Xdurable:husd        T2TRUE:Xdurable:husd  \n",
       "\n",
       "[203 rows x 7 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coef_df['variables'] = variables\n",
    "coef_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "d26e3cb1",
   "metadata": {},
   "outputs": [],
   "source": [
    "coef_df = coef_df[(coef_df['variables'] == \"T2TRUE:Xfemale\") | (coef_df['variables'] == \"T2TRUE:Xblack\") | (coef_df['variables'] == \"T2TRUE:Xfactor(dep)1\") |    (coef_df['variables'] == \"T2TRUE:Xagelt35\")]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "78682f88",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>coef</th>\n",
       "      <th>std err</th>\n",
       "      <th>t</th>\n",
       "      <th>P&gt;|t|</th>\n",
       "      <th>[0.025</th>\n",
       "      <th>0.975]</th>\n",
       "      <th>variables</th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>T2TRUE:Xfemale</th>\n",
       "      <td>-0.6598</td>\n",
       "      <td>0.550</td>\n",
       "      <td>-1.200</td>\n",
       "      <td>0.230</td>\n",
       "      <td>-1.738</td>\n",
       "      <td>0.418</td>\n",
       "      <td>T2TRUE:Xfemale</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T2TRUE:Xblack</th>\n",
       "      <td>0.4526</td>\n",
       "      <td>0.510</td>\n",
       "      <td>0.887</td>\n",
       "      <td>0.375</td>\n",
       "      <td>-0.548</td>\n",
       "      <td>1.453</td>\n",
       "      <td>T2TRUE:Xblack</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T2TRUE:Xfactor(dep)1</th>\n",
       "      <td>0.7636</td>\n",
       "      <td>0.785</td>\n",
       "      <td>0.972</td>\n",
       "      <td>0.331</td>\n",
       "      <td>-0.776</td>\n",
       "      <td>2.303</td>\n",
       "      <td>T2TRUE:Xfactor(dep)1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T2TRUE:Xagelt35</th>\n",
       "      <td>1.1233</td>\n",
       "      <td>0.637</td>\n",
       "      <td>1.762</td>\n",
       "      <td>0.078</td>\n",
       "      <td>-0.126</td>\n",
       "      <td>2.373</td>\n",
       "      <td>T2TRUE:Xagelt35</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "0                       coef  std err      t  P>|t|  [0.025  0.975]  \\\n",
       "                                                                      \n",
       "T2TRUE:Xfemale       -0.6598    0.550 -1.200  0.230  -1.738   0.418   \n",
       "T2TRUE:Xblack         0.4526    0.510  0.887  0.375  -0.548   1.453   \n",
       "T2TRUE:Xfactor(dep)1  0.7636    0.785  0.972  0.331  -0.776   2.303   \n",
       "T2TRUE:Xagelt35       1.1233    0.637  1.762  0.078  -0.126   2.373   \n",
       "\n",
       "0                                variables  \n",
       "                                            \n",
       "T2TRUE:Xfemale              T2TRUE:Xfemale  \n",
       "T2TRUE:Xblack                T2TRUE:Xblack  \n",
       "T2TRUE:Xfactor(dep)1  T2TRUE:Xfactor(dep)1  \n",
       "T2TRUE:Xagelt35            T2TRUE:Xagelt35  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coef_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "a9929168",
   "metadata": {},
   "outputs": [],
   "source": [
    "errors = coef_df['coef'] - coef_df['[0.025']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "f56f5e54",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-43-ed9be35b4c99>:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  coef_df['errors'] = errors\n"
     ]
    }
   ],
   "source": [
    "coef_df['errors'] = errors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "0f13573a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-44-2521eb0ca58a>:17: FutureWarning: The pandas.np module is deprecated and will be removed from pandas in a future version. Import numpy directly instead\n",
      "  ax.scatter(x=pd.np.arange(coef_df.shape[0]),\n"
     ]
    },
    {
     "data": {
      "image/png": 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mNfarvt9LueDZiPO5lGP3SpTq4x+mfA62pXy2vsrQ/2I3RvFZb3rtCW2XTFf2OKrV45p+bq28tyxeSUmYoFy9fnFLz9gfI+IY4IwqhiMj4uTMbG7EHs7QnJ53ZOZnW37HHyLi6Or116OcNJ6QmY9UVQTnRcQaTdvPa60eGBFPb7p5TYfVBRsno0Fgn8z8WdNj50XEz4BzKcnN6yPi0yP17FVD775PaUxfRRlG2JzonA/8sGkY3PMoSd5Rw7zkOsAhLVdxz4+I4ylDp9YFdo6IdTLzlqqxd9lI/zeGyoL/DXhho5FYOTMifklZAuNRlBPyl+v+7mZVwnM05cSyCNg7M5t7dC4AfhIRR1IKf6xLSYR2B2gMbYyI5mHFf1vW4aktn5U6V7S5Qvp9ypXU24CdWoYnXggcFxEHU3o3nkJZO3PxcgVRlj/Ysbr5S+BlLVek/xAR36bsC8+gJBXbUa4Qk5mXRcTdTdvfMoZKmp2YQRmq+JymnpjFF6aiFHn5n+rmcZQe+ebP0J+qffskymf8yIj4adN7uwFDx4YPZGbrEPtfR8TfKI2QVSgNqNbjx3BOovROQGkYndPyeCNxfISSaO3U5jV2Yaja5RKJY9WDs3n1/NbiMiNpHp76Usqw9eHs1vRzc6Go5ouFq1F6Ir8G/IXS+N6JMqSvse8uoqWRlZmnVsfv11Aaas1X+K9lyXVzP0L5P3263VD/ZRFlKPujqps3j1cPQvW636R8jh8Ads4lq8CeX/UMfZOSfDyF0vg/fJiXXIdStfv9TfddUB0nL6BcZHgyJSE7vxr2eVkVS2O/uK/DffbA6vWg9EI192KdHxE/oYxGae2davaxKq5FLH2Og5IsHkvZt1elJBrbDvNa61A+77tm5kNN958SEf+mXPxagZJQ/2/jwShD2RvFlK4GdmwZkfGHiDidkpTPpAylfF713PUYuphxDrBLS+//edXn+DuUhO01EfHjzOx0JMBoPZpyIWDbXLLS8ZkRcTlDSfQBjZ+rfeaWiFi8xNMwn4Mxt5favNYM4IeZ2Zy0NRLXH1Den5mUdt5SF7+qnu6dq5s/y8wHmx7+cPV9AWW/ar1w+YuIOKXp9/03S14ErjNh7ZLpzB5HtWouYNB23PsYvbP6/jDlhLPUsKrMvJGhHsmBpuc0rp411l88q81BsPEaNzDUSFmfoWR1QkXEkxm6Cvu9NidUqpP/+6qbM6i/OtfwKoZ6Bw5tSRqbHclQj+Zbal7vwnZDfzLzbkqjAcr//mmjiK1ZI8YbWw7Ojdf/RxXjpyhXkTspGb8bQ8MPv96SNDb/jg9TLjwA7Fa9JxPp0lF+LVEUJCJewNCcsvcMN6etep8avcmHRsTyzS9Dmcv0CPDBdsOYqmS1eTmZ1iGxk+3nNcP33k7ZJ+6jLP3T7jP0IKUhsIjyGX1z08PNC98PN6/paMrFnf9h5PmAzb93HiWJgpZ9tjou7VTdbAy/enKUAi3NGsMnL8+ll8JozBk6Z4Rh5u2cSpk3BHB4RGzVbqOIOIwl9+nmz9LTm37+P2CrzPxmZv4pM0/PzMYIgxurbd4QZc5dq9dRitL8naE5RF+nNIrvqOJ4OiURuItyLGjE94qIOCkiLo6I4yPiZSP+5e1N1PnrdZQeFSgXJpZaOiTL3NDDGBq9cXDVYG7nX5Qe99bXmE/pfWnYrHWbZdCoXfBv4LDWY0aVZL+OprmozaqLh42RJz9od46rXudSyrBXgGdEfZGhw1uSxobmxKD1f/Bahj6/r283jD8zT2PoAs22VeIP5YLGypSLu69pM2S8cdx8I0OFnurOp+Pho22OCVDmjTdi6OhzMEHtpa+3u7NKZBsjfvasLna3egVD79niYapR5j2uSPk7T2qTNDZ+x8kMzaPu5Dw2ke2SacvEUa2ae0ZWGHarDkSZ9Nw48P0qWwp1NKuGPjSKOLywOgBCafQ0hlqNtP7ebygnBhiaYzPRmq/mHzvsVmUy+ubAqu0SuDYawwkfYSgpWkp1svttdXOT6spqO6cOcz+UnoGG1gqII7my+v6iiPhY1YvSGuNXMvPdVaO0k/kaOzf9PNKVxq81/fyiYbfqruYhoiN9ln9dfV+NoeUFyMyvZubjgRWzviJlc4/OuOzPy6Btb1h1sm4M1bwga4qZtCRxzfv2NQxVmfxcROzZkmiTmQ9l5sGZ+dF2Df8RNIarbtPUCIUyR+hR1e9uJEIDNPU6VkM7G72SdcNUO+7VqBrdjXmjK1KGSL49Ih4TEctHxMZR1tn9CmXdv4bmRtSnKcfn3SgVSJfqpasu6jX3Nry1zTaLMvMLmfnkzFwhM9fNzMNyycIbn6D8fz7ZeJ8j4jOU3pRdKQVS9gB+HhFtG6ojGPfzV6VxDFpIufjQVtU4bTy+Iu17nwH+UNMbuizH4baqc/AW1c2TcpjCNdUFxKWKt1V2oiRdMPrjFgx/Dr45hy90Vfc/aJxr/5HDz7mFcmHp8cDspgSxcey9PmvmwVcXcBojIp7deiwZZ23PydU5slHoqNPPwXi3lxZQP4WhMVx1Nkue3xoa81hvpGkObWY+kJlbZeaalOHwdRrnsk7264lsl0xbDlVVq7uafl5r2K06s2nTz3VDqZq3eTJlnsdjKUNmm4dTfTgiPtzmee08fpTbLauNm36+ZLiNqsbCZR28buPvXh5YGEOVQEfyeNrP3atbv7C5MdHpseEzlIbfcpRhREdExCWUHrPfAWe3u+I3So3Pz30MJQ3Daf58bTrsVuMgM8d6dbL5szyvw/f0Ty0xLILFydd61TZPpAyV25olh4l1+0LhTcPc/ziGhhc+N5au+DqcDRs/ZOadEfEtyhDKx1Aav/dWQ9ZOA07NzGz/MqNyMmWo8HLAcyjzAGEoIbwoM6+KiOso78FODFWa3Z6haqOtw1SbRx6MaThcZh4TpZrmBynDAz9TfTW7njJUsVEg6f6m5z9AGa5VuyRGZp4WEddT/u/PjWGqZg8nIp5NuZhzM9XQ32r+2dspF8beQBmm/BzKxbc3RMTpmfnT9q/Y1r8pjeABxu/8BUPHkqySqzqtx6BftNlmoo7DwwmGhkpfNsK2F9C+gF3zcevYakjqaAx3Du74f1Ad5zaqbg57ngXIzOYLJY0LOI1iRI/v4DizMmVI6bAXvJfRDTWPNf4PnX4Oxru9dMcwPcMNJ1B6BNegDFddPCooyhzqxlqz3xvumNF0LluFcox5AmWk0WbV8xuJcCfnsYlsl0xb3W5IqPdc0/TzeA1taz6B3zaK7W9t+rnx3LXH+LsfNfIm4+LR1fdFmfnv2i07M95/9/3D3A9DVx1hqJExKpl5DmVuTOPkOoPSG3MEZR7LnRHx44jYfpiXqNP4DNwxioZq8+drPBuO42lc3tOIGIiIvSPiNErBgX9Qrub+H2WY0g4sXZ20m5YaFlYZ6/9juShL3zS8hVKco9GTM5syz/VLwJURcU111bl1GOlonMvQ0Mfm4aqNxPH0lu87NW3TGKb6T0pBk2bbUD6n/8zMyxmjzPwQ8ELKHNbm5TRupcw/2owl11hsPsZ2ohHjbDo/tjaWc/lw0xynxlzJ72bm0Zl5T5aKt41tO1oPOEul6sYx6NHRvpDPWDSOJWM9f7WakONwjUc3/XznCNsO99mYtHNRy3G++X+wFkNJ1Eh/R7s4xtrmnah2xMM1Pc8w9Fno9HMw3u/VcMduYPHIh8aFsl1aRmW8kqH4215siIjHRsTnI+JaSrJ8BaWQ3yer5zcKDnZkgtsl05Y9jmp1EWU4zkyGH2YzrIjYl3J183Tg3OpE3nywHs1VvubGbuNg0fxZPYzSkBuNB0feZFwscVV0HIc8NF73zyy9DmWduqu5EyIzT46I31J6FfakDO9qXHxYlbJe2d4R8YnMfG/7V2mr8fkZzf+0+bM2lvXoJkPjPb2D0c1zbVh8BT0iVqRc1W0eFtQY2nQlpVfhHEq1zKXK0Y+z0TbGhnv/mvfto+msQMEDjR+yFHX4fxHxv5RhT7tRkufGnJsnUK46HxYRO2fmBa0vNpzMXFQVaHgVVbJYzeVpVKhsThwPpMxz/K/MvI2hxPGkNseFxnDq37KMMvN3lAq8q1F6n/8D/KvxO6NUfW4Y6/HhgaafZw27VYuI2INSnOlqynvc0KgK/MeWpzR6RreJNkugjOB8SkNzRUphqNZiRnVxrkvpuT2dsmxKY3hco+E71vNXt3VyPhpu+Ynm/XRPhua8jmTYoedjsCxt1ubnnkJZzmO0rhl5k54y3u2l0Xx+jqHMgV2Rkqw1hq82qqmem23W1YyIF1HOZc0Fuxprgv6VUizuNMookroqvW1NYLtk2jJx1BIy8z8RcTalYuNGEbFhZnbSyHgjpTF1JKWB9XuWvDL46HZPatG8zV0t3wEW5MRWgByLRnwzKMM1xqvX8S7KBO+1e/BvXkrVeD+p+mo0Vp9POWA/l9IAe09E/CYzR7vcS+PzM2cUSXm7z06vacT1KODKEYYADedDDCWNF1Eq052RLcVVopSeH6vR9ny0rmnXqeb3acVl/ZxXjf2vAF+JsnbsMym9cftQhsWuQVmSY5PsbLH7kymJY1TDrzamzLeZz1By0rxW5o4RcR5DjZ1xnd84nGo+V7segsaw5QVUS7VUQ2WfS1k37qFsWWy8jcYcoYWMcv+qfkejyu37W5LAxlzs1l6uRu/uCpSLH6Pp6Wv4FUPzpV5IB4kjZVjbIdXX9ymVNaH8resx9vNXtzUXkFlqnleL4Qr6NP8tD3TpfNR8Xu10RElz/Kv1w/l0GUx6eykzz4mIayhDifehLAH1FIaGBy/V2xgR61DmN69COS59nLLkTbae56N+eZuRYpuIdsm0ZeKodr7LUKn/wymV8kYUpaR+4wr8LQwVc2meO1NXYa2h0cC5j6Felua5bc+hZn2lqvrbWylX1S9bliFgHWie5L8ZwxSyqRpRV1Madidk5gdHeN2/UBLH9SJi48y8argNI+LllCtpNwCnZWbdcKhxVVUP3IRSdGBxIyUzr6T0gH0lIg6nrFkFpYE22gP0nymfq1UpjfC6eY7Nc/quHHar7voLJc6ZlHW7hi1YFGXZjc0o7+kfM/OOKMuTNCok3g08L9tUB6zMXYY4mxv4Kw+71bL9DoDrKD1ZK1OSrRl1CV1EvJ1ybLg2S/XExn61AbBhZi5O3qoRD6dTisZ8kLJfbkuZBxqUq9qjdQrlf9IodtOYY3l+NU+QzPxXRFxJ2Reey1Dj9n6WTCqJiLUow6YWMnIBi7aqYg9vpCQrx1W9ju22G6AUnYEyr+fBKt5FEfFzSvJ/S0QcP9yFmSoJbxRo+nMHc4NeTdlvL2FoONtImtsmnQ63/gVlePJsyiLwn8wly/+3Vf2PmpcRaa5I/GdK4hgRscYI8xx78Rh0JUMjibYeYdunD3N/6zl42M9sRDyeUhDlekrBq+EqHXckMx+uhjM+gREqjUbErpRiatcBR7QkNltFxOzWC20tzz+IcuHiBuC3HfZ6d1u32kvHUi5iPrca+fDK6v6HGVpapNmrGJr//ZEsldHbxbgCo7to0/q8iWyXTFvOcVQ7P2BoKNObRjP+uxo611zx8lNZrV2XmbcylDzuGsNX/CQinkNp0AH8vqkBeSFDV9H2ioj1l3rykEMpCwR/l6HG0kQ7rennfYfdqsxpejzl5DyaBlHzELa3DrdRNaH8G5Q5Xj+gs6FJo1HXkN+e0it4DvWL6DZX2luxg9/dnFi9ftitijc0/TymxvgkGO17OkAZtvkZlhzKM4ehXr5rhksaq0qAzQtHt14oHKm37e6mnx9Xs92Lax4bUXU1uDHU8zHUVNeriql8hrJOafOwom9SGoi/rxqt7X7Pwyy5TmInn0GyVAFtVGN9ASXppyn2hkaC+ByGhqme2qZneWfKOfj8ZZgX/RBljc/XM7RmWTt7M/Qetg5dblQ5XIfSQzec1zH0uftxzXaLRcQsSu84wHvbJKWNnsbWc0JjKNkCOpzLVu0PjYbgesDnR/nUtzGUjFzGkserxjFoJmUoclvV39t4HxZQUwl7MmXmnQwNB9412lSXhMUN9Fe0e4yy7zSSp9e1zC9u9W7Kep3fpwxRHk+Nc+0GEfHMmu12pbz/2zNUkbNx7J1Fzbmqal98nTJy4cs9mjTWHb+71V76HqXtMYtyXmgcy08a5hi3UdPPrfO/m72MoeP1qDq8JqFdMm2ZOGopVUPuIMqBaTngV1VvVlsRsTZlGFZjDbELGVowu+Fz1fcVgB+1G3ZQDf9qzH8ZbHpOo8x54zVXAH4aEUsNkYuILRlaK/Ehaq60jafMPI+hctWvi4ilGmDVSbmxXMQCllyvazjfZmjY2RsiYu82rztA+TsbQ4y+3egBGUcPN/3c+t5dwNBQsjdFxEa0t1/Tzxd28LtPZGiOyWER0XaB6oj4H4Z6yn/fw0ORfklJcgBeHBHvGma7TzC0fuUJWZZEgDJnqNGQeUq13yyhasAexZJzQlrLmNe9p1D+543P0R7tGh8R8VZgy2Hi70TzOmNfiTZrcEbEoyiFfxq+1PTzyU0/fy7arMcVZc2wPaub9zK07mknGr/n+Qwtpj5c4hgMJWLthqk25jeOeZhqlSQ1LpC8PCKW6k2KiE0ZWvT8GsqFpWbNS9h8qTqet77GMxhabuQWSqI+GodReoLPyMx28zgbx8zdW+5vDMO+fIxVDz/OUG/y6yPiG9XFtaVEKTL1Vob+vkco6wM2J7lHM3Qh5UPVBYzW15lB+T8/sbrrmBx+3dJu+EL1fWVKVdQljgfVPvMllk7igcVDwH9Y3Xx09RpLzXONiJcwlFzfwuh7mUfrqwxdGP16dVxojWG7phhOz6GlN77E0JItH4pS6bf1uStQEt7lmp7TixYfv1vbU91qL2VZF7JxIeqdDHUCDFeBt3mpnrYXICNiG5ac9z7a5Tgmul0ybTlUVW1l5h8i4lBK43M14GcRcT5l/PlllAblOpSr6q+lzBuCMgzzZVXy2exYyuKyu1Ou1P85Ij5Pucq0HKXB/1aGkp9PZuaZLa/xCYbW+tq2eo3PUXb2VSjFfA5nqGfm3dlmceAJdBClFPuKwMlRlgc4gTJM7SnAOxhqVHwya9aRasjMuyPiEMoV/gFK0r17dft2ypCdNzJ0Vfd62iwsPQ6a/49viIgrKA2syzLzgYj4COXg/ijggoj4MqVIRWNu0Msp8x6gDBFpN2ylrcxcGBGvplwxn0VZ4+37lAbJbZSG6YEMrbV2B50VEppU1d+zPyXhWB7434jYEfgOZcmK9Sn7VKO36t809Uxm5oMRcRKlAMFKwBkR8SlKJboVKL3ZBzOUdDa0NhzuoLyHywOvjIjfURrHV2fmXdWwsJ9SSvPPBs6MiI8xNHz61ZR9ujF0bMwy8/Qo6/YdSulRvSAivkLp6Xmk+pvewdCw2F9W1TcbTqQcB7amXDW/sHq9qyn7zSaUdd0aifSnx3hx5SRKj2cjuXqIpQtPnM7QkhArUC7A/ap5g6qR3vi8/mYMcTRrzCdfjlIc51OUq+yNIbVvphwTHwb2r3peF8vMUyLih5SREhsDl1av0TiuvoSSAK5AeS8OGGGoZuNvnM1Qr/ARw2x2DOVz/LKI+BKlZ30nynEcxtiQrfaRl1AasY+lzFl8aUQcQzmO3EK5WPJ0yrHi6dVTFwCvay2clJn3VEMXGz3/Z0TENymfh3so/7c3MjQV4ypqRhN0Q2aeFBE/ogwf3Jmyj3yGcjxen/IeP5dyvmqbZFN6ZXei7Id7ApdExBcpx541KcesQyg9s4OUdUHHtUBdZl4REZ+kFLd5GnBZRHyW8nldjdImeQvl8/9g9XPjuVdFxHsplYZXBE6rztMnUi4mPYkyNadxnLiAoYsuvab5nPzxKMujLMzMS6v7utVeOobSnmtcULyd4Y9xP6ccIwYoF4VXpizJcwdl1MEelM9r8zqaq8UoChBm5vyJbJdMZyaOGlZmfjMi5lGSxw0oJ8W6OYo/Bw5rd5U1Mwer3rJvU67wbEj7K3kLKFfAPt3mNR6qevJ+SjnBzWXoKmqzhcCRmTmpVwoz8/KI2Jly4FubciI+rM2mX6I09kb7uj+JUlb+m5SrxfvSfjjsX4HdRtOo61RmzouIiykng6cwNATruZSGcuNK++GUg/Rwf9/fgF3aXFgY6fefF6X62k8oicX+1VerS4C9s2UNr15TzbfZhXIBYC1Kg2uXNpv+A9izupLb7M2Uxu6G1Ve7xdL/Q2kEfZPSkFuiIl2VwJ5IGQa0HkM9X6+jJLEA76KMJNiUMtSxtSGflOTxr8P9rR14MyUReyulMfNuhha3b/YLytyYoSDKXL29KEPRnkz5nLZbqH2Q8r/66FgCrBqeVzN0AehPbRKxuyLicoaSkXPbHBO3oBQpuZ36IVqjiemCiHgd5b1ZnaFCNM3uAPbJzOGqK76OkuC+ipJotTt23gW8dpiew3beTtlXj8/M84eJ/YSI+AllKO2bq6+G37AMPSCZeX1EbFW9xm5VLO+ovtq5Hjgwh1lUPjOPi4hXUj5Xq1CWCmm3XMjvgFdn5n1tHuu2Ayjv836UoiXHtDx+A2Vo7xdpI8t6qTtSlkrYjHJMadf7/CBwaGae0Oax8fA+Svu1cTGpXbx3Uc4FVzTfmZmfirKG48cpCclw5+mzgJd2eq6aRCdQzrMzGdp35lHaat1sL/2c0tvZmBf/o+GG+mbmZRHxfsoxa4BywfS1bTb9NaXexcGUi8cbUS4KjmRC2yXTlUNVVSszT6FcTd2X0tt4JeXK3ELKldbLKQeJLTPzv+uG5mTmQ5n5KkqP4/coJ+qHKL0cl1HW7NkkMz813NWkzLwzM59H6bn8KeVA+VD1dRVlnt/mmfnxZfvLx6bqJd2IcmK7gPK3PUI56P0EeHZmvmWkq2VtXvcHlLmRH6FcMfw3Q/N//kCZ27dFdlYBt1O7UYa53Ub5m/5F6XkiMwcz8y2U9/ZYynvxQLXdPymNwIOAzZqGXHakatBtROm9OIfSMJhPaeycTJmbs21m9kXp9CyFXR5PSY7OojTuF1A+M+dQkrYnZ+ZSiUWVGG9BSYCuoDTUGp+Hcym9zpGZRzPUI7ZLm+F6r6MMVbyZ8l7dRtMaYNX+vDWlp+ECyr5/H2V/fS9loel5Y/0ftPxNCzPzbdVrHkU51tzH0P5zHPCSzHxZu16MzPwH5X9yKKXxfgvl83Ef5fP4TWC7zHxjp/tfi+ZhsW2TDJYshFNXTfXUZYwFgMw8lvK3H01ZJuERSs/RJZT5S5GZv695/sOZ+WrgeZTj1E2U/93dwKWUghdPysx2f8tSoqyV+TZKkvK+ETZ/FWWfvqb6nddRGnh7ZmdVb5eSmbdm5u6Uz/BnGVqP8xHKPnMj5XO1H+XcM9z72Xi9H1N61z9KSfjvppx7rqGMgNgF2DnLvP6ek5nzq3PwzpTe0n9QeqKvpfSkb045Xte9xg2UCzOvpny2/0l53x6gHIs+BzwlM1uT0nFTnW/eVcXxf1X8D1ZfV1B6255cHWPbPf/TlGGUn6PUX2gM/7+VkqTsC+yUZW5oT8pSxGZ3ymf6Psrf/kjVa9fYZtLbS1kKDv2i6a7hhqk2tv84Zc74iZT//wLKZ+layj71ksx8CUv2Bg43D7f1tSe8XTIdDQwOjncNDUmSJEnSVGKPoyRJkiSplomjJEmSJKmWiaMkSZIkqZaJoyRJkiSplstxFCtQqq79i6HFYSVJkiRpupgJrEup4P9w64MmjsXWlHL4kiRJkjSd7QCc3XqniWPxL4B///t+Fi1yeZJetNZaq3Lnnb24nrLUP9yPpGXjPiQtG/eh3jZjxgCPetQqUOVGrUwci4UAixYNmjj2MN8badm5H0nLxn1IWjbuQ32h7dQ9i+NIkiRJkmqZOEqSJEmSapk4SpIkSZJqmThKkiRJkmqZOEqSJEmSapk4SpIkSZJqmThKkiRJkmqZOEqSJEmSapk4SpIkSZJqmThKkiRJkmqZOEqSJEmSapk4SpIkSZJqmThKkiRJkmqZOEqSJEmSapk4SpIkSZJqmThKkiRJkmqZOEqSJEmSapk4SpIkSZJqmThKkiRJkmqZOEqSJEmSapk4SpIkSZJqLdftACRJkiRNbR894XJmzZrJu168abdD0RjZ4yhJkiRJqmXiKEmSJEmqZeIoSZIkSapl4ihJkiRJqmXiKEmSJEmqZeIoSZIkSapl4ihJkiRJqmXiKEmSJEmqtVy3A5AkSep1Ll4uabqzx1GSJEmSVMvEUZIkSZJUy8RRkiRJklTLxFGSJEmSVMvEUZIkSZJUy8RRkiRJklTLxFGSJEmSVMvEUZIkSZJUy8RRkiRJklTLxFGSJEmSVMvEUZIkSZJUy8RRkiRJklTLxFGSJEmSVMvEUZIkSZJUy8RRkiRJklTLxFGSJEmSVMvEUZIkSZJUy8RRkiRJklTLxFGSJEmSVMvEUZIkSZJUy8RRkiRJklTLxFGSJEmSVMvEUZIkSZJUy8RRkiRJklTLxFGSJEmSVMvEUZIkSZJUy8RRkiRJklTLxFGSJEmSVGu5bgewLCJiJnAo8BrgScBM4Drgx8CnM/OhLoYnSZIkSVNC3/Y4VknjCcCXgU2A84AzgPWADwNnRMTKXQtQkiRJkqaIvk0cgYOAlwB/BjbJzOdn5ouBJwLnAs8A/qeL8UmSJEnSlNDPieMB1fe3ZubNjTsz8w7K8FWAfSY7KEmSJEmaavo5cbwDuBK4oM1jV1Xf15u8cCRJkiRpaurb4jiZuVvNw1tX3/8xGbFIkiRJ0lTWzz2ObUXEAKU4DsBx3YxFkiRJkqaCvu1xrPFxYEfgVuDTnTxxrbVWnZCAND7mzJnd7RCkvud+JI3NrFkzAfchaazch/rflEocI+LDwBHAw8ArMvP2Tp5/5533sWjR4ITEpmUzZ85sbr/93m6HIfU19yNp7ObPX8isWTPdh6Qxch/qfTNmDNR2pE2JxDEilgO+ChwCPATslZlndjcqSZIkSZoa+j5xjIhVgZ8BLwLuBvYwaZQkSZKk8dPXiWNEPAr4HbAlcBOwS2b+pbtRSZIkSdLU0reJY0TMAn5NSRr/BuycmS6/IUmSJEnjrG8TR8qSG9tSehp36rQQjiRJkiRpdPoycYyINYHDq5u3A5+PiLbbZuarJisuSZIkSZqK+jJxBLYBVqp+3qL6Go6JoyRJkiQtg75MHDPzFGCg23FIkiRJ0nQwo9sBSJIkSZJ6m4mjJEmSJKmWiaMkSZIkqZaJoyRJkiSplomjJEmSJKmWiaMkSZIkqZaJoyRJkiSplomjJEmSJKmWiaMkSZIkqZaJoyRJkiSplomjJEmSJKmWiaMkSZIkqZaJoyRJkiSplomjJEmSJKmWiaMkSZIkqZaJoyRJkiSplomjJEmSJKmWiaMkSZIkqZaJoyRJkiSplomjJEmSJKmWiaMkSZIkqZaJoyRJkiSplomjJEmSJKmWiaMkSZIkqZaJoyRJkiSplomjJEmSJKmWiaMkSZIkqZaJoyRJkiSplomjJEmSJKmWiaMkSZIkqZaJoyRJkiSplomjJEmSJKmWiaMkSZIkqZaJoyRJkiSplomjJEmSJKmWiaMkSZIkqZaJoyRJkiSplomjJEmSJKmWiaMkSZIkqZaJoyRJkiSplomjJEmSJKmWiaMkSZIkqZaJoyRJkiSplomjJEmSJKmWiaMkSZIkqZaJoyRJkiSplomjJEmSJKmWiaMkSZIkqZaJoyRJkiSplomjJEmSJKmWiaMkSZIkqZaJoyRJkiSplomjJEmSJKmWiaMkSZIkqZaJoyRJkiSplomjJEmSJKnWct0OQJI08T56wuXMmjWTd714026HIkmS+pA9jpIkSZKkWiaOkiRJkqRaJo6SJEmSpFomjpIkSZKkWiaOkiRJkqRaJo6SJEmSpFomjpIkSZKkWiaOkiRJkqRaJo6SJEmSpFomjpIkSZKkWiaOkiRJkqRaJo6SJEmSpFomjpIkSZKkWiaOkiRJkqRaJo6SJEmSpFomjpIkSZKkWiaOkiRJkqRaJo6SJEmSpFomjpIkSZKkWiaOkiRJkqRaJo6SJEmSpFomjpIkSZKkWiaOkiRJkqRaJo6SJEmSpFomjpIkSZKkWiaOkiRJkqRaJo6SJEmSpFomjpIkSZKkWiaOkiRJkqRaJo6SJEmSpFomjpIkSZKkWiaOkiRJkqRaJo6SJEmSpFomjpIkSZImzIPzF3DPg/O57Z4HOStv5cH5C7odksZguW4HIEmSJGnqGRwc5KRLb+L4i+fxyMJFDALHnHU13znzavbcci67bb4+AwMD3Q5To2TiKEmSJGncnXTpTRx/yTzmL1y0+L6HFpSfj79kHgC7bzG3K7Gpcw5VlSRJkjSuHpy/gOMvnsf8BYvaPj5/wSKOv2QeDz2ycJIj01iZOEqSJEkaVxddfyczRhiFOmNggIuuv2NyAtIyc6iqJEmS1IPWXHMVZs7sz36evebMZq/tN1p8+53HngvAp/ffrlshTYiFCxdx1133dzuMSWHiKEmSJPWgmTNncPvt93Y7jDE5K2/lmLOuXjynsWHnj/xq8c8rLj+TA3bYiO03fvRkhzdu5syZ3e0QJk1/XsKQJEmS1LO22nAtFg3Wb7NocJCtNlx7cgLSMjNxlCRJkjSuVpq1HHtuOZdZy7VPN1ZYbgZ7bjGXFZefOcmRaawcqipJkiRp3O22+foAZR3HRYsYHCzDUxctGmSPLeYuflz9wcRRkiRJ0rgbGBhg9y3m8oJN1+PIX1zKILDnFnPZasO17WnsQyaOkiRJkibMSrOWY/WVZjFr1sy+LoQz3TnHUZIkSZJUa8okjhFxQEQMRsT23Y5FkiRJkqaSKZE4RsR2wJe7HYckSZIkTUV9nzhGxF7Ab4FVux2LJEmSJE1FfVscJyIeC3wceDXwAHAr4GxbSZIkSRpn/dzj+FFK0ngRsC1wZXfDkSRJkqSpqZ8TxyuB1wDPyMwruh2MJEmSJE1VfTtUNTM/2e0YJEmSJGk66NvEcSKstZb1dXrZnDmzux2C1LdmzZoJuB9JY+U+pG6ZKp+5qbwPTcW/qR0TxyZ33nkfixYNdjsMtTFnzmxuv/3eboch9a358xcya9ZM9yNpjNyH1A1Tqf0zVfehqfQezZgxUNuR1s9zHCVJkiRJk8DEUZIkSZJUy8RRkiRJklTLxFGSJEmSVMvEUZIkSZJUy8RRkiRJklTLxFGSJEmSVGvKrOOYmTt1OwZJkiRJmorscZQkSZIk1TJxlCRJkiTVMnGUJEmSJNUycZQkSZIk1TJxlCRJkiTVMnGUJEmSJNUycZQkSZIk1TJxlCRJkiTVMnGUJEmSJNUycZQkSZIk1TJxlCRJkiTVMnGUJEmSJNUycZQkSZIk1TJxlCRJkiTVMnGUJEmq8eD8Bdzz4Hxuu+dBzspbeXD+gm6HJEmTbrluByBJktSLBgcHOenSmzj+4nk8snARg8AxZ13Nd868mj23nMtum6/PwMBAt8OUpElh4ihJktTGSZfexPGXzGP+wkWL73toQfn5+EvmAbD7FnO7EpskTTaHqkqSJLV4cP4Cjr94HvMXLGr7+PwFizj+knk89MjCSY5MkrrDxFGSJKnFRdffyYwRRqHOGBjgouvvmJyAJKnLOh6qGhGzgH2A8zLzquq+3YD/BeYCFwBvz8xLxzNQSZLUX9ZccxVmzuzPa9R7zZnNXttvtPj2O489F4BP779dt0IadwsXLuKuu+7vdhiS+kRHiWNErAmcDQRwCHBVRGwM/BxYvtpsJ+CMiNgiM68dx1glSVIfmTlzBrfffm+3wxiTs/JWjjnr6sVzGht2/sivFv+84vIzOWCHjdh+40dPdnjjYs6c2d0OQVIf6fQy4LuATSjJ40XVfYdRksajgVUpCeVs4L3jFKMkSdKk2mrDtVg0WL/NosFBttpw7ckJSJK6rNPEcXfgZuD5mXl5dd+ewCDw8cx8IDO/DVwCvGDcopQkSZpEK81ajj23nMus5do3lVZYbgZ7bjGXFZefOcmRSVJ3dJo4bgCcn5mPAETEkyjzGq/LzOuatrsO+K/xCVGSJGny7bb5+uy5xVxmzZxBY7nGFZefyayZM9hji7KOoyRNF50Wx7kfWLHp9i7V99+3bLcu8MBYg5IkSeq2gYEBdt9iLi/YdD2O/MWlDAJ7bjGXrTZc255GSdNOp4njlcD2ETEHuAvYjzJM9aTGBhHxDGA74MzxClKSJKlbVpq1HKuvNItZs2b2bSEcSVpWnQ5V/RawGvBX4Frg6cD1wCkAEfE1Su/jAHDUuEUpSZIkSeqajhLHzPwe8FZgZcrcxiuBvTJzYbXJTpQKq2/NzJ+OX5iSJEmSpG7pdKgqmfmliDgKWD0zb295+BDgL5l593gEJ0mSJEnqvo4Sx4jYH7g2M88BWpNGMvPsars9gC0z88hxiVKSJEmS1DWdznH8LqVXcST7A2/vOBpJkiRJUs+p7XGMiHdQ5jM22ywi6noSVwdehMtxSJIkSdKUMNJQ1ZWAD1KW3Biovj8VeNooXvsbyxSZJEmSJKknjJQ4fgpYQBnSOgB8GLgMOG6Y7QeBh4CrgZPHJ0RJkiRJUjfVJo6Z+TDwicbtiDgIOD0zPzbRgUmSJEmSekNHVVUz83ETFIckSZIkqUd1vI4jQESsBGwCrEJNZdbMPHOMcUmSJEmSekSn6zjOpAxdPQSYPcLmg52+viRJkiSp93Sa2L0NeEf1cwK3UIrnSJIkSZKmqE4Tx9dSEsUXZ+bvJyAeSZIkSVKPGXZ+4jA2BP5g0ihJkiRJ00enieOdlLmLkiRJkqRpotPE8QTgmRHx6IkIRpIkSZLUezpNHN8H/AM4OSKeExGrTEBMkiRJkqQe0mlxnFMpyeYWwGkAEbGI9sNXBzNzhWULT5IkSZLUbZ0mjlu1uW/meAQiSZIkSepNHSWOmdnp0FZJkiRJUp8zEZQkSZIk1ep0qOpiEfEMYEdgfeDyzPx2ROwKnJ+Zt49XgJIkSZKk7uq4xzEiHhcRZwN/Aj4BHAY8u3r4SODGiNhr/EKUJEmSJHVTR4ljRMwB/gg8E7gI+Dgw0LTJX4EVgJ9ExObjFaQkSZIkqXs67XF8P2Vo6vsy8xmZ+T/ND2bma4EDKZVWjxifECVJy+LB+Qu458H53HbPg5yVt/Lg/AXdDkmSJPWZThPH3YErM/MTw22Qmd8F/gxsswxxSZKW0eDgICdeMo83HnMet9z9ILfc/SDHnHU1bzzmPE68ZB6Dg+2W4JUkSVpap4njusBfRrHdNcA6nYcjSRovJ116E8dfMo/5CxfRSBEfWrCI+QsXcfwl8zjp0pu6Gp8kSeofnSaOdwIbjWK7jYG7Og9HkjQeHpy/gOMvnsf8BYvaPj5/QUkeH3pk4SRHJkmS+lGnieMfgM0iYvfhNoiIPYFNgdOXIS5J0jK46Po7mTFQv82MgQEuuv6OyQlIkiT1tU7Xcfwo8FLgZxHxJeCM6v5VI+KZwC7A24H5wKfGK0hJmmxrrrkKM2d2vGJRz9hrzmz22n5ogMg7jz0XgE/vv123Qhp3Cxcu4q677u92GJIkTQsdJY6ZmdUajT+mJIhvAwaBPaqvAeAB4IDM/PM4xypJk2bmzBncfvu93Q5jzM7KWznmrKt5qGWo6s4f+dXin1dcfiYH7LAR22/86MkOb1zMmTO72yFIkjRtdHw5PTNPpcxhfC/wO+BK4GrgTOAjQGTmz8czSElSZ7bacC0WjVA0ddHgIFttuPbkBCRJkvpap0NVAcjMO4BPVl+SpB6z0qzl2HPLuaWqapsCOSssN4M9tpjLisvP7EJ0kiSp34wpcZQm00dPuJxZs2byrhdv2u1QpL6y2+brA3D8xfN4ZNEiBgfL8NRFiwbZY4u5ix+XJEkaSW3iGBFXUeYw7pyZN1S3R2swM2OZopMkjdnAwAC7bzGXF2y6Hkf+4lIGgT23mMtWG65tT6MkSerISD2OG1ESx1lNt0drhNk1kqTJsNKs5Vh9pVnMmjWzbwvhSJKk7hopcdyw+n5zy21JkiRJ0jRRmzhm5o11t5tFxArAgsxcOE6xSZIkSZJ6QMfLcUTEmhHx1Yj4QctDuwC3RcTXImKV8QlPkiRJktRtHSWOEbE2cD5wKLB1y8OzgBWB1wPnR8Qa4xGgJEmSJKm7Ou1xfB/wBOD/gG2aH8jMnwBrVY89Gfif8QhQkiRJktRdnSaOuwBXAYdk5t2tD2bmQ5TeyOuAvZY5OkmSJElS13WaOK4P/Dkzh11qoyqOcymw3rIEJkmSJEnqDZ0mjrcBTxzFdnOBuzoPR5IkSZLUazpNHH8HPC0iXjPcBhHxckrhnNOXJTBJkiRJUm+oXcexjU8DewNHR8QuwMnATdVjjwVeDLwCeBD42HgFKUmSJEnqno4Sx8y8KiL2Ao4B/ht4ecsmA8CdwKsy86/jE6IkSZIkqZs67XEkM0+LiI2BPYCdgHWr17kFOAf4SWbeO55BSpIkSZK6p+PEESAz7wd+WH1JkiRJkqawTovjSJIkSZKmmdoex4i4ChgEds7MG6rbozWYmbFM0UmSJEmSum6koaobVd9ntdwejcHOw5EkSZIk9ZqREscnAIuAm6vbG05sOJIkSZKkXjNS4ngKcFpmvrG6vQFwa2bmxIYlSZIkSeoVIxXHWR9Yq+n2GcD7JiwaSZIkSVLPGSlxfBDYJiLWm4xgJEmSJEm9Z6Shqn8E9gRuiggoBW/2i4j9RvHag5k5pnUiJUmSJEm9Y6TE7s3Ao4CtgRUY6qFcNJFBSZIkSZJ6x0iJ4/zMfE7jRkQsAr6fmftPbFiSJEmSpF4x0hzHiyPip023bwSunsB4JEmSJEk9ZqTE8b9abm8APHGCYpEkSZIk9aCRhqreA+wQEc8E/lHdt0pEzB3Ni2fmvGUJTpIkSZLUfSMljr8GXgOcVd0epFRZ3XMUrz04iteXJEmSJPW4kRK7t1bfG1VVnwDcB9w6gTFJkiRJknpIbeKYmfcAr23crqqqHm9VVUmSJEmaPjodSvoh4PKJCESSJEmS1Js6Shwz80ONnyNieWBzYH3glsw8JyLmWhBHkiRJkqaWjovXVAnjB4A3AqtVd/8AOAf4fkSsDOyTmdeMW5SSJEmSpK4ZaR3HJVRJ42+A9wCzgD8BA02brAJsAZwVEeuOV5CSJEmSpO7pKHEEDgeeC5wEbJCZO7Q8vh3wbeDRwLuWPTxJkiRJUrd1mjjuD9wGvDIz72h9MDPnA4cCNwE7L3t4kiRJkqRu6zRxfCJwdmY+ONwGmbkQuAjYYFkCkyRJkiT1hk4Tx4eAOaPYbp1qW0mSJElSn+s0cbwI2CYiNhlug4h4CrBVta0kSZIkqc91uhzH54DnA7+OiDcDZzQeiIgB4HnAUdXrfnWcYpQkSZIkdVFHPY6ZeQpwJPA44ETgP8Ag8FLgAeC3wOOBL2TmieMaqSRJkiSpKzrtcSQzPxoR5wHvAHYAVqKs3/gIcDYlafzluEZZIyKeD7wXeBplbcmLgU9m5m8nKwZJkiRJmso6ThwBMvM04LSImAGsBcwE7szMR8YzuJFExAHAd4CHgT9UcTwHOCUiXp+Z35zMeCRJkiRpKuq0OM5iETEL2JbS67glsFlEjCkRHePvX5cyn/IeYKvM3CUzdwaeRRlC+8WIeMxkxSNJkiRJU1XHiWNELBcRHwfuAM4CfkaZ73g+cFtEfCIilh/fMNt6M7AC8PnM/Evjzsy8EPgUsCJwyCTEIUmSJElTWkeJY0TMpCSJ76bMbTwf+DElebwYWA14F3DC+IbZ1ouq78e3eawxx/LFkxCHJEmSJE1pnQ4tPYSSsF0I7JOZ1zc/GBFPAH4E7BwRr83M74xPmEuqlv54MrAI+HubTa6qHntKRAxk5uBExCFJkiRJ00GnQ1UPAO4FXtKaNAJk5rXAS4D7gAOXObrhPYoyTPXOzJzfJo4FlKG0KwOzJzAOSZIkSZryOk0cnwyckZl3DLdBZt4OnFFtO1FWqb4/ULPNg9X3VScwDkmSJEma+gYHB0f9tfHGG9+38cYbnzCK7U7YeOON7+vktTuM4zEbb7zx4MYbb3x9zTY3Vts8ZhSv+bjBwcHBwQ02GByE8nXRReWrcRsGBz/wgcHBwcHBwXXXHbpviy3KfQcfvOS2N988OHjiiUve941vlG2b79t113Lfrrsuef/gYNm++b4TTyyv23zfwQeXbbfYYui+ddct933gA0tu28d/091rrD3l/qap+D75N/Xu33TTBjHl/qap+D75N/X233TujrtPub9pKr5P/k29+Td9+AsnTLm/acq9T7/73WDlcYODS+dMA4ODg6NOMiPiImAjYMPM/Pcw26wJXAdcnZlbj0t2u/TvWB24G7g1M9cZZptbgf8CHpWZd4/wko8Drr/zzvtYtGj0/49eNmfObG6//d5uhzEuPnrC5cyaNZN3vXjTbocyrqbSe6TeN1X3I2myuA9Jy8Z9qPfNmDHAWmutCrAhcMNSj3f4et+lVE49ISLWa30wIh5Lqag6G/heh6/dif9Q5lGu3W7tyOq+tYGHRpE0SpIkSZJqdFpV9evAy4Adgesj4jxKNjoIPB54BrA88Efga+MX5pIyczAi/gZsA2wM/K1lk6AkxVdMVAySJEmSNF101OOYmQspy3F8AVgA7AC8Gtgf2B54pHrsxVVl04l0SvV9zzaPNe779QTHIEmSJElTXqdDVcnMhzPzbcBawLOBfYBXVj/Pycy3ZeZD4xtmW98BHgLeHRFbNu6MiK2Ad1Gqqk5Yr6ckSZIkTRejThwjYreI2LtxOzMfysyzM/OnwK3AV4BXRUTHyehYZOYNwNspcy7PjYjfRMQpwJ8ocywPyczbJiMWSZIkSZrKRkzyImKViDgDOB44cJjNngM8DTgKOLOqrDrhMvNrwG7AeZRhs1sDZwMvyMzvT0YMkiRJkjTV1RbHqXoPfws8E7it+rmdbwMPA2+utj2eMnR1wmXmycDJk/G7JEmSJGk6GqnH8bWURPAC4CmZ+dl2G2XmTZn5cUqP3xXAsyJiv3GNVJIkSZLUFSMljvtSKqXum5l3jvRimfkPSqEcKNVWJUmSJEl9bqTEcTPgosy8brQvmJl/Ay4EtliWwCRJkiRJvWGkxHFV4OYxvO4NwBpjeJ4kSZIkqceMlDj+C1hvDK/7aOD+MTxPkiRJktRjRkoc/w48NSJWH+0LRsRqwJbAVcsSmCRJkiSpN4yUOP4AmA28p4PXfA+wCnDKWIOSJEmSNHW8f4/N+PT+23U7DC2D2nUcgZ8D7wfeGREPAJ/IzEfabRgRywFHAO8G7gK+Mp6BSpIkSZK6ozZxzMyHI+IVwJ+ADwAHRcQvKVVTbwWWB+YA2wAvAdYHHgJ2y8zbJzJwSZIkSdLkGKnHkcy8IiK2oAxb3Qp4U5vNBqrvZwFvzMy/jF+IkiRJkqRuGjFxBMjMq4FtImJ7YG9gE2BdYAGl8urFwAmZeeFEBSpJkiRJ6o5RJY4NmXk2cPYExSJJkiRJ6kEjVVWVJEmSJE1zJo6SJEmSpFomjpIkSZKkWiaOkiRJkqRaJo6SJEmSpFomjpIkSZKkWiaOkiRJkqRaJo6SJEmSpFomjpIkSZKkWiaOkiRJkqRaJo6SJEmSpFomjpIkSZKkWiaOkiRJkqRaJo6SJEmSpFomjpIkSZKkWiaOkiRJkqRaJo6SJEmSpFomjpIkSZKkWiaOkiRJkqRaJo6SJEmSpFomjpIkSZKkWiaOkiRJkqRaJo6SJEmSpFomjpIkSZKkWiaOkiRJkqRaJo6SJEmSpFomjpIkSZKkWiaOkiRJkqRaJo6SJEmSpFomjpIkSZKkWiaOkiRJkqRaJo6SJEmSpFomjpIkSZKkWiaOkiRJkqRaJo6SJEmSpFomjpIkSZKkWiaOkiRJkqRaJo6SJEmSpFomjpIkSZKkWiaOkiRJkqRaJo6SJEmSpFomjpIkSZKkWiaOkiRJkqRaJo6SJEmSpFrLdTsASdLEe/8emzFnzmxuv/3ebociSZL6kD2OkiRJkqRaJo6SJEmSpFomjpIkSZKkWiaOkiRJkqRaJo6SJEmSpFomjpIkSZKkWiaOkiRJkqRaJo6SJEmSpFomjuppD85fwD0Pzue2ex7krLyVB+cv6HZIkiRJ0rSzXLcDkNoZHBzkpEtv4viL5/HIwkUMAsecdTXfOfNq9txyLrttvj4DAwPdDlOSJEmaFkwc1ZNOuvQmjr9kHvMXLlp830MLys/HXzIPgN23mNuV2CRJkqTpxqGq6jkPzl/A8RfPY/6CRW0fn79gEcdfMo+HHlk4yZFJkiRJ05OJo3rORdffyYwRRqHOGBjgouvvmJyAJEmSpGnOoapT1MKFi5gzZ3a3wxiTvebMZq/tN1p8+53HngvAp/ffrlshTYiFC9v3qEqSJEm9xsRxirrrrvu7HcKYnZW3csxZVy+e09iw80d+tfjnFZefyQE7bMT2Gz96ssOTJEmSph2HqqrnbLXhWiwarN9m0eAgW2249uQEJEmSJE1zJo7qOSvNWo49t5zLrOXafzxXWG4Ge24xlxWXnznJkUmSJEnTk0NV1ZN223x9gLKO46JFDA6W4amLFg2yxxZzFz8uSZIkaeKZOKonDQwMsPsWc3nBputx5C8uZRDYc4u5bLXh2vY0SpIkSZPMxFE9baVZy7H6SrOYNWumhXAkSZKkLnGOoyRJkiSplomjJEmSJKmWiaMkSZIkqZaJoyRJkiSplomjJEmSJKmWiaMkSZIkqZaJoyRJkiSplus4SpIkjeD9e2zGnDmzuf32e7sdiiR1hT2OkiRJkqRaJo6SJEmSpFomjpIkSZKkWiaOkiRJkqRaJo6SJEmSpFomjpIkSZKkWiaOkiRJkqRaJo6SJEmSpFomjpIkSZKkWiaOkiRJkqRaJo6SJEmSpFomjpIkSZKkWiaOkiRJkqRaJo6SJEmSpFomjpIkSZKkWiaOkiRJkqRaJo6SJEmSpFomjpIkSZKkWlMicYyID0bEYEQ8ttuxSJIkSdJU0/eJY0TsCbyv23FIkiRJ0lTV14ljRBwG/BRYrtuxSJIkSdJU1ZcJV0RsAnwW2AW4A1gBmN3VoCRJkiRpiurXHsejKEnj74Atgbu6G44kSZIkTV39mjheCOyemS/MzHndDkaSJEmSprK+HKqame/sdgySJEmSNF10PXGMiB9QhpuO5JeZ+Z6JjkeSJEmStKSuJ47ABkCMYrt1JzqQtdZadaJ/hcZg1qyZAMyZY/0jaVm5H0nLxn1IWjbuQ/2r64ljZm7f7Rga7rzzPhYtGux2GGoxf/5CZs2aye2339vtUKS+NmfObPcjaRm4D0nLxn2ot82YMVDbkdavxXEkSZIkSZPExFGSJEmSVMvEUZIkSZJUy8RRkiRJklTLxFGSJEmSVMvEUZIkSZJUq+vLcYyHzHxct2OQJEmSpKnKHkdJkiRJUi0TR0mSJElSLRNHSZIkSVItE0dJkiRJUi0TR0mSJElSLRNHSZIkSVItE0dJkiRJUi0TR0mSJElSLRNHSZIkSVItE0dJkiRJUi0TR0mSJElSLRNHSZIkSVItE0dJkiRJUi0TR0mSJElSLRNHSZIkSVItE0dJkiRJUi0TR0mSJElSLRNHSZIkSVItE0dJkiRJUi0TR0mSJElSLRNHSZIkSVItE0dJkiRJUi0TR0mSJElSLRNHSZIkSVItE0dJkiRJUi0TR0mSJElSLRNHSZIkSVItE0dJkiRJUi0TR0mSJElSLRNHSZIkSVItE0dJkiRJUi0TR0mSJElSLRNHSZIkSVItE0dJkiRJUi0TR0mSJElSLRNHSZIkSVItE0dJkiRJUi0TR0mSJElSLRNHSZIkSVItE0dJkiRJUi0TR0mSJElSLRNHSZIkSVItE0dJkiRJUi0TR0mSJElSLRNHSZIkSVItE0dJkiRJUi0TR0mSJElSLRNHSZIkSVItE0dJkiRJUi0TR0mSJElSLRNHSZIkSVItE0dJkiRJUi0TR0mSJElSLRNHSZIkSVItE0dJkiRJUi0TR0mSJElSLRNHSZIkSVItE0dJkiRJUi0TR0mSJElSLRNHSZIkSVItE0dJkiRJUi0TR0mSJElSLRNHSZIkSVItE0dJkiRJUi0TR0mSJElSLRNHSZIkSVItE0dJkiRJUi0TR0mSJElSLRNHSZIkSVItE0dJkiRJUi0TR0mSJElSLRNHSZIkSVItE0dJkiRJUi0TR0mSJElSLRNHSZIkSVItE0dJkiRJUi0TR0mSJElSLRNHSZIkSVItE0dJkiRJUq3luh2ANJL377EZc+bM5vbb7+12KJIkSdK0ZI+jJEmSJKmWiaMkSZIkqZaJoyRJkiSplomjJEmSJKmWiaMkSZIkqZaJoyRJkiSplomjJEmSJKmWiaMkSZIkqZaJoyRJkiSplomjJEmSJKmWiaMkSZIkqZaJoyRJkiSplomjJEmSJKmWiaMkSZIkqZaJoyRJkiSplomjJEmSJKmWiaMkSZIkqZaJoyRJkiSplomjJEmSJKmWiaMkSZIkqZaJoyRJkiSplomjJEmSJKmWiaMkSZIkqdZy3Q6gR8wEmDFjoNtxqIbvj7Ts3I+kZeM+JC0b96He1fTezGz3+MDg4ODkRdO7tgfO6nYQkiRJktRlOwBnt95p4lisAGwN/AtY2OVYJEmSJGmyzQTWBS4EHm590MRRkiRJklTL4jiSJEmSpFomjpIkSZKkWiaOkiRJkqRaJo6SJEmSpFomjpIkSZKkWiaOkiRJkqRaJo6SJEmSpFomjpIkSZKkWiaOkiRJkqRaJo6SNIVExMqj3G4gIt46weFIkqQpYrluByANJyLWBXYA1geuycwTImIr4PLMfKS70Uk967cR8aLMvH+4DSLiqcC3ga2AL0xWYJKk6SkitgG2BFYG/gGcnpm3dTcqdcrEUT0nItYAvgLszVCv+A+AE4AvARtExMsz89zuRCj1tGcBv6uSx/80PxARKwAfAN4OLA/c1IX4JElTTEScCfw2Mz/Wcv8TKW24LVue8khEfAt4Z2Y+NElhahmZOKqnRMSqwBnA04Cbq5/3a9rkXmBd4NSI2Dwzr5nsGKUe92NgH+C0iHhhZt4NEBE7Ad8ANgIWAp8BPtidEKXeERHrLcvzM/Of4xWL1Me2B25ovqMaOfZHYB1Km+4PwC3AhsALgcOATSPi+Zm5cFKj1ZiYOKrXvJuSNH4DeEtmzo+IxYljZu4cEUdSGrzvAQ7sSpRS79oPuAd4PfD7iNgbOAJ4LTAAnAMcmpl/6V6IUk/5BzA4xucOYltKGs4HKUnjD4GDM/PBxgMRMQf4DvBi4C3A57oRoDrjwU695hXAPOBNw119yswPV8nkDpMamdQHMnMQODQi7gHeBWT10F3AuzLzO10LTupNHwbeS2kT3UvZVyQtu12B22hJGgEy8/bqwub1wP6YOPYFE0f1mrnASaMYsnAFsMskxCP1pcw8IiL+A3wUeAR4Xmb+ucthST0nMz8YEZcBPwEeAnbIzH90NyppSlgD+ENr0tiQmfdHxFnAzpMalcbMxFG95l5KFdWRbFBtK01rEfHMmofPoBQl2A/4eUQcAsxv3iAz/zRx0Un9ITOPj4j/RynM9i3K8DlJy+ZqYNYI26xNmXevPuA6juo15wBbRcS2w20QEdsDWwA2eCU4Gzir5mvfaruNgN+3PHbmZAcr9arM/BrwG+CFEeGIFqlzT42IfSJi4+r294AdI2Juu42rC5/PAi6drAC1bOxxVK/5JGVM/K8j4n8oPSYAA1Xlu12AT1AKEjgeXirJ31gLe0ha0tso68ztCPy6y7FI/WYzyigXIuI+4CpKj+NJEfGczLyreuwplJoWb6cUbftyd8JVpwYGB21vqLdExAHAUZR15toZBN6emV+ctKAkSZLUVkQ8Fti85avR0zgIPD4zb6y2/Rnwsuqxz2bmOyc5XI2RiaN6UkRsArwV2Iky53Em8C9K78qXMvPirgUn9YGIWB6I5mU3IuLxlJP5bzLzga4FJ0ma8iLiUQwlkZ+rqn5TLav2NOAbmfm7LoaoDpk4StIUExEvBb4NXJ2Z2zbdvz/wXeB2YL/MPK07EUr9JSJWp0zvuSczF3Q7HknqBuc4StIUEhHPAn5OqVJ3RcvDlwPHUKqs/joidsjM8yc5RKmnRcQKwB7ACymjXh7L0NSJRRFxA6U424mU5aPmt3kZSS0iYhbweMr+lO47/cceR3VVRJy6DE8fzEzX/pGaRMSvgRcAO2fmH4bZ5rnA74BfZ+Zukxmf1KsiYjbwDuCNwKMoRTugrO14D2XKxJoMVaQfpPTefwr4+nBr1UnTSUQ8gXLRZQFwfGbeXt3/TuC9wGrVpvdTlr850l78/mHiqK6KiEXL8PTBzJw5bsFIU0BE/BP4W2Y+f4Tt/gg8LTMfNTmRSb0rIvYBPg88GvgLcBLwR+DyzLy1abuBapvtgGdTeiYfB9wGvDEzj5vcyKXeERHvAz7I0MWVeymV8jejVE4dBK4FHgaCcjHmV5m5+6QHqzFxqKq67TndDkCaYmZTekdGchuwwgTHIvW8iPghsA9wHPCpzLxwuG2r4h63AL8EfhkRbwNeBLwJ+FlEfC8zXzMJYUs9pVr79COU888PKD2LL6es5QilwOFejekREbEh8GPgJRFxUGZ+e/KjVqfscZSkKSQiLqHMyXrccJVTqzlc1wF3Z+ZTJjM+qddExAXA4Zl53jK+zk6UpQW2HJfApD4SEb+l9MJvkZl/r+7blTIXeBB4RWuPfEQ8Bvg78JfMfOYkh6wxmDHyJlLviYjlI8K5WdLSfgisDfw4ItZsfTAiVgOOBdahXO2VprtnLGvSCJCZZ5g0ahrbCjizkTQCZObJlMQQYKkq3pl5M3A+8NRJiVDLzKGq6jlVQvgmysKxsxgqUADlYseKlMIFy1HGx0sa8iXgFZR5Jf+IiHOBm6rHHgtsC6wMXAZ8thsBSr2ksbacpGWyMtCuSurfgScxfGfVgprH1GNMHNVTIuKFwPEsmSy2cy9w+oQHJPWZzJwfEc8DPgYcwNLziB8GvgW8c7ihrJIgIlYG9gR2BNajLHFzE3Aq8BsrQUpLuArYPiLWzsw7mu5/FaUj4KHWJ1RDVbdnqFdSPc45juopEfEbYGfgC5QFzF8OHAlsDTxSPfYB4C7g6Zl5d1cClfpAtWbWVsC6lAuFtwAXZ+Z9XQ1M6nFVoY9vUYZ0t17IHASuBPbPzIsnOzapF0XE4ZS229+BI4BTMvORYbadQVmy40vAEyhzjL86SaFqGZg4qqdExO3APZm5UXV7a8r4970z82fVfS+hlEr/ZGa+t2vBSpKmnOq8cxZlqsTxwAmUnsYBYANKL+SuwJ3ANpl5fVcClXpIlQx+l9LDOAg8NTP/Nsy2P6JMqRgAfgXs7pDx/uBQVfWa1Skn7Ia/Vt+3AH4GkJm/ioi/UUqgmzhKbUTEutTPE14H2C0zX9aF8KRediSwPPCazPxem8ePjoiDgG8C7wcOnMzgpF6UmYuA/SPiZGA/ytDV4dwEXEPZh75o0tg/TBzVa/5DOWEDkJkPRMStwJNbtvs7rgEpLaVaauMHwEu7HYvUp7YFzhsmaQQgM78dEYdQLmBKqmTmT4GfjrDNu4B3TU5EGk9WMVKvuQLYpmr8NvydMsex2X/hhQ+pnbcDe1Eq1V0M3FjdfzpwaXX/AJCAvY3S0panzAceyQ2URc4ltYiIIyNiz1Fsd3BEfGcSQtI4MHFUr/kxMAf4XUQ8q7rvt8Cjq4PQ8hHxckoVrrphENJ09QpgEbBDZm4DvK+6/22ZuRVljtafgI2Af3YnRKmnnQU8NyLmDLdBRKxEOQ+dM2lRSf3lg4xu5MuuwN4TG4rGiz026jXfBnYDdgHeQTkpH0XpRflA9dXwhckOTuoDT6AMs7ugun0BpYfxWcCfM/OWiHgFcB1lH3tFd8KUetZbgLOB0yLigMy8tPnBiFgPOJrShjq0C/FJPSciPges0XL3MyPi6JqnrQ68BLh1ouLS+DJxVE/JzIXArlWv4szqvnsi4jnAV4BtKJOqP5+ZP+xepFLPWh64uen29ZSlbJ7auCMz/xkR5wCbTXJsUj/4DDCPcr65KCKupRTyWAg8BtiUcn66B/htRDQ/dzAzA2n6uRb4ctPtQcqFzCeM4rlfnJCINO5MHNWTMvPnLbf/isVwpNG4BXh040ZmLoqIG2hKHCt3Ac+YxLikfrFn088DlGHdG7XZbg2W7mGxOqSmq69TziszKPvNsQyNGmtnEHgIuDozr5iUCLXMTBwlaWo5B3hFRGzZtDj5FcBLImLtzLyjWm9rc+DubgUp9bANux2A1G+q5Th+1LhdLVnz28z8Qfei0ngbGBz04ph6S0S8CDiEcoV3xZpNHRIktagWLz8XmA98KjM/GBG7URYxvwD4P8qckt2AX2bmy7sWrCRJ6hv2OKqnVKWbj2PJBcuH41UPqUVmXhgRr6bMNXl8dd9JEfErSsK4NWX/uouhiquS2oiIdYEdgPWBazLzhIjYCrg8Mx/pbnRS74iIfZfl+dat6A8mjuo176c0aj8O/JwylM4EUepAZv4oIo4D1mm6ew/g1QwVmDo2M12OQ2ojItagFGTbm6Gly35A6bn/ErBBRLw8M8/tToRSz/k+y9ZeM3HsAyaO6jUBnJ2Z7+92IFI/y8z5lMqQjduLgGOqL0nDiIhVgTOAp1EqFJ8B7Ne0yb3AusCpEbF5Zl4z2TFKPehYvNA/5Zk4qtf8m3JSljQK1ZpyY2avo7SUd1OSxm8Ab8nM+RGxOHHMzJ0j4kjKAufvAQ7sSpRSD8nMA7odgyaeiaN6zY+A10fEupn5r24HI/WBfzD2q7yDeB6QWr2C0lv/pmpt4aVk5oerZHKHSY1M6lMRMQCsSSlseFe349HY2GBQr/kAsB3wx4j4MHA5cOdwG9tbIjEPhwdJ42kucNJwSWOTK4BdJiEeqW9FxPOBtwPbAytT5kK+JiJ+BtwI/E9mPtjFENUBE0f1moeBS4DDGXkulr0lmvYy83HdjkGaYu6lVFEdyQY4tUIaVkR8BHgvpejhgup7o2r+5sBewLYR8fzMfKg7UaoTM0beRJpUHwTeXP38IKX647xhvm7qQnySpKntHGCriNh2uA0iYntgC+BPkxaV1Eci4qWUJZ+uoywFtVrLJi8FLqWMMnvD5EansbK3Rr3mAOAhYO/MPLnLsUh9KyLmAq+jzMFaD3iEMh/ydOA7mXlHF8OTetkngV2BX0fE/1CqqgIMVMWodgE+QRn18rmuRCj1vrdQOgCel5nzACJi8YOZeUVE7ExJLF8FfKELMapD9jiq18wBTjdplMYuIt4AXAn8D/AcyjI3mwIvojSKr4qI3boXodS7MvN84GDKfKwvAX+mJIn7Uka6fAN4FPD2zDyrW3FKPW5z4I+NpLGdzLwTOAt4wqRFpWVi4qhecw2wareDkPpVRLwI+Fp187PATsBGwBOB51MavbOBn0XElt2IUep1mfld4OnAt4CrKSNhHqFMk/g+8IzM/GK34pP6wExGV7hteRwB2Td8o9RrvgJ8LSKel5m/73YwUh96N6UIwc5tekOuBf4QEacAvwQ+RBmSJ6lFZl6Jc6+ksfo78IyIWCMz7263QUSsCWxDGSGjPmDiqF5zBnAK8JuI+AVwPnAX5UrvUjLzh5MXmtQXtqIMDxp2CF1mnhgRZ1PKo0uSNN6+C3wZ+FFEvLp1Xn1ErAV8j1I05/uTH57GwsRRveZKytCGAcoizP89wvYmjtKSHgYeGMV2w66PKk0nEXH0Mjx9MDMPHLdgpKnjKMqIlp2BGyPib9X9z4yIU4GtgdWBsxmaXqEeZ+KoXnMsLmYuLYtfAXtFxNzhihJExNqUuY+/m8zApB51QJv7ms9DA8M8PlB9N3GUWmTmwqoI2weANwGNOfWPr74epPRIHpGZbUeVqfcMDA7aRpekqSIi/otSpW4WcATws8xc1PT41sA3gTWAHTLzH92IU+oVEbFfy10DlH3nScBPgROAGyhzh9elrEn3OuBCSqPXyqpSjYiYRVn3dH1K0Zx/ARdm5mhGx6iHmDhKUh+LiPlt7h5gqGr2QuBmytXddRlahPlfwD2Z+ZQJD1LqIxFxKKVQ276Z+ZNhttkFOAl4f2Z+YjLjk6RuMXFUT4qIJwGHAztSrlAdl5kHRMRXKPMgv5qZfng17UXEopG3Gl5muiyT1CQirgTuzMxnjbDdGcDczHz8pAQm9ZGI2H8Umw1Sih/eA1ybmVdNbFRaVs5xVM+JiIMp495nNd3daNw+DzgU2DEi9m4egidNRyZ+0ribC/x5FNvdSSnwIWlp36XDmhVVAZ3XZuZFExKRlpkNDvWUiHg2pRLXfyiTqTdu2eTtlAWY9wJGczVL0jAi4jHdjkHqQTcCz46IVYbboCow9Vzg6kmLSuov+wMXUKZOXAX8L+XC/2HAJ4ArqseuA74AnAxsApwaEY+b/HA1GiaO6jVHUAoQvCAzv5aZ1zQ/mJm/Bp4DzAcO6UJ8Uk+JiG9FxEpjeN4+jK5XRZpufgD8F3ByRGzU+mBEbAb8ljJf+JuTHJvULxYAzwC+CDwlM9+bmd/MzG9k5vsz8+nAhykVVs/KzD2A3SiF297ZpZg1Auc4qqdExF3AJZn5/Kb7FgHfz8z9m+47BdgyM+d0IUypZ1T7x9+AvTPzr6PYfjZlzax9ATJz5sRGKPWXiFiBkhg+mzLU7jpKgSmADShDWQeAn2bmPl0JUupxEXExMBuIupoUEfFX4P7M3Ka6fRGwRmYuddFG3WePo3rNisD9o9huAbDyBMci9YOfAE8GLoyIN9RtGBHbA5dTksZFwMcmPjypv2Tmw8ALgHdRksYnUJLIZ1MSx78Dh5g0SrWeBFw6ikKGfwE2bbp9HbDehEWlZWJxHPWaa4CtI2KF6uS9lGpY3tbAtZMamdSDMvOVVXXHzwNfjYjnAQdl5j2NbSJiJvARyvCfmZT9bP/MPK8LIUs9r1qQ/DPAZyJiPYYasv/IzFu6F5nUN24HRrPc05OBe5tur9JyWz3EHkf1mh8D6wBHVQvGLqG672vA2sDPJzk2qSdl5jeAbSmFOl4GXBYR2wFExMbAecC7KUnjUcDTTRqlot25pllm/jMzL6q+apPGapirJPgd8KSI+OBwG0TE+yjJ5WnV7dWAZ2HRqZ7lHEf1lIhYETgb2IIyp+Q8SkP4z5TqXM+lTKT+O/CMzLyvS6FKPaeqAvlVSjW7BcAPgZdThnX/CzgwM0/pXoRS74mIa4G3Z+bxy/g6rwQ+kZmPG4+4pH4WEXMp7bY5wCXAiZSq+DMo84R3pbT17qaMIrsNuAh4InBoZlp4qgeZOKrnRMTqwFeAfSg9JK1OAg7OzNsmNTCpT0TEq4FjGFpD63jK8NV/dy0oqUdFxGeBtwLnAp8GThrtGsHV1Il9gDcCmwGfzcwjJihUqa9ERADfArav7mqckwaq7xdT1m38S0RsSukkOAl4WWYumNRgNSomjuqqiNgVuDozs81j61GKEaxPSSD/RSnZfN3kRin1j4h4MWWJgMdQTtIDlIXKD8/MH3UzNqlXRcSOlGHcG1PmZp0MnEUpJnUDZW3hGZRpEusC21HOTzsDq1NGwbw+M8+e7NilXhcRTweeT+lpXB74B3B6Zv6paZvVgXXatQfVO0wc1VURcSslGXx5dfto4E+Z+e3uRib1l4hYmVIg5yBKsng6cDDwJuAt1WY/pgwB+k9XgpR6WFVE6kBKEaknMNQ7MpwBSsL4UeDHo6geKUl9zcRRXRURDwGnZubu1e2l1myUVC8itgWOpTR25wPvz8zPNj2+C/BdYC3KHJNX2zMiDS8idqAsybEjpZdkDqWn5C7gKuBPlCGtfxr2RSQ1LsisDcxiaIgqlB78FSkFEXfLzLd3ITx1yMRRXRUR11EOGh+lDF34LuWEPKpJ0Zl57IQFJ/WBiPgwcARleaW/APtl5hVttlsH+D6lwNRC4H+BD2TmwkkMV5I0DUTEAGXO8OsZxbrbmdmupoV6jOs4qtuOBj5MWWMOytCg7aqv0TBx1HT3fsp+8wXgiMyc326jzLwlIl4AvAf4YPX9hcA2kxOm1B8i4itAZuaXux2L1McOA95W/XwHJedYHbgeWANYs3rsOkpBRPUBE0d1VWZ+NCLmURqvK1Dml1wD/LGrgUn942bgNZn5h5E2rOZgfTwiTqcs1bHlRAcn9aFXAQmYOEpjtz/loubLMvP4iHgt8G3gRZl5TURsSRkFMxdwmag+YeKorquGmx4LEBEHAudl5sHdjUrqG0/rdJmNzDy3qnLnOllSe7d2OwCpz20CXNK0Pup5lDmOOwLXZObFEbEn8Ffg3cBruxGkOjOj2wFILZ4DfHw0G0bEYyY4FqnnjXVtxsy8JzP3Hu94pCngm8DOEfGSbgci9bEVKcNSG66mzK9/WuOOaumNc3HKRN+wx1FdFRHfoqwv9yBAZo5qiGpE7AN8lVIlUpKk8TKP0uA9MSJuoKzleBewqM22g5n5+kmMTeoXd1DmMgKQmQuqqUmbtmz3L2CzSYxLy8DEUd12ILBdROydmX8daeOImA18Ddh3wiOT+lC1pM0i4MmZedUw2wTwN0qj1/OAtKQvNf28YfU1nEFK1UhJS7oAeFFEPC4zb6ju+yuwU0Sskpn3V/c9CXigGwGqczYY1G0/AfYGLoyIt2XmUcNtGBHbU+ZCbkBpGH9ickKU+s7AyJuMahtpOnKulbTsvg7sAZwfER/MzK9T2ny7Aj+OiC8BuwFPAX7bvTDVCddxVNdFxOuBz1Oqqv4COCgz72l6fCZluY53AjMpVVf3z8zzuhCu1NMiYsfqx/Mz86FhtlmJak7JaIeHS5LUiYh4F6X9dlxm7lu1586jVPQepFzAnA88OzMv6F6kGi0TR/WEiHga8FNgY+BGYN+q8uPGwA+ALSgHmKOAd2SmwxokSRMqIh4LPBtYB3iYUm31rMy06qo0ChGxLvDozLysur0ypSNgG+Am4GuZ+efuRahOmDiqZ0TEKpSCN/sDCyjrzL0cWJkyefrAzHStH0nShIqI1SgXKl/B0sO6FwE/Bw4ba1VjSUMi4jGZeXO349DITBzVcyLi1cAxlGEMAMdThq96gpZaRMSzgVursuadPO9gYLvMfN3ERCb1p4hYETiLMpzuHuA3lCqrM4HHAy8EVgMuBp6ZmY90KVSpZ7RWye/gefsAX81Mq+T3AYvjqKdExItZch3HAcowoRcBP+pKUFJvOwP4HvCa1gci4hfAGZn5pdbHKPvVvoCJo7Skt1KSxpOBV2Xmf5ofrHojfwDsArwR+MIkxyf1IqvkTwMzuh2ABGXMe0R8g3KifgxwOvBEygl5TeD7EfGD6oQtaUnDVUjdE9hqEuOQpoJXUtage2Vr0ghQ3bcPcCfwqkmOTepVPwGeTKmS/4a6Dasq+ZdTksZFwMcmPjyNBxNHdV1EbAtcBhxEqa71zsx8XmZel5lvo5RrvpNyor68OuBIkjQRnkgpgHP/cBtUj51FKegmTXuZ+Urg0OrmVyPiZxGxevM2ETEzIj5O6Rx4HHAtsENmHjmpwWrMTBzVVRHxYeBMYCPKwrDbZOZnm7fJzF8DT6McaDYATo+Ij1ZlnSVJGk+PUIqyjWRlhubiS9NeZn4D2Ba4GngZcFlEbAdQVck/D3g3Zb7wUcDTXVqtv5g4qtveTzmAfAHYKjOvaLdRZt4CvKDafhB4D3DuJMUoSZo+/gw8OyLWH26DiNgA2LHaVlKlWlpjS+BYysX+P0bEd4FLqvtvAXbJzMNcWq3/mDiq224GXpCZb8vM+XUbZuZgZn6ccrKeRzkASZI0nr4BrAT8NiK2aX0wIp4BnAKsAHxrkmOTel5m3p+ZB1CKti0HvJqyT/0S2NSl1fqXVVXVbU/rdJmNzDw3Ip4OfHNiQpIkTVeZ+f2I2IUyr/7ciPgHcANltMvjKQXcBoCfZOaxXQtU6mFWyZ+aTBzVVWNdmzEz7wH2HudwJEkC2I8yH+v/UYbbNQ9bvRH4PPDlLsQl9bSIWJmyfxxESRZPBw4G3gS8hVIlf1fg0HZVi9XbTBwlSZKaZOYg8CXgSxHxWGA9SiP4n5l5U1eDk3pUVSX/WOAJlCr5728qePi2iDgN+C6lN/+ZEfHqzDy7K8FqTAYGBy0Ipt4REYsoa/o8OTOvGmabAP4GDGamFz80rVX7zJgP5JlpdWKpSUQcCfw5M48fYbuDgWdm5msnJTCph1VV8o+gdEr9BdivXcHDiFgH+D7wXGAh8L/ABzJz4SSGqzGyOI560XCLmbduM5rtpOlgYIxfkpb2QeClo9huV5wyITVYJX8asLdGveY51fd5NdvMa9pOmu427HYAUj+LiM8Ba7Tc/cyIOLrmaasDLwFunai4pD5zM/CazPzDSBtWQ8E/HhGnAz/EKvl9w6GqkiRp2oqIN7JkoZtBRt8jf0Rmfmr8o5L6S0Q8aiwFDyNideCbmWnvfR8wcZQkSdNWRMygDDmdQUkYjwXOAY4a5imDwEPA1cMNx5OkqcjEUV0VEc8Gbs3M7PB5BwPbZebrJiYyqT9ExP6j2Owh4G7gxk73NWm6qYbPnZqZn+h2LJLUS5zjqG47A/ge8JrWByLiF8AZmfmlNs97NrAvYOKo6e67dFBVNSJuBT4JfLmaZyKpSWY+BxZXf3xKZv6+8VhEPBV4IfDzzLyxSyFKPc8q+VOTb5J6wXBzSfYE7pvEOKR+dCYjJ44zgdmUtbXWoSzO/GTgDRMbmtSfIuKtwKeAK1iycMfWwKeBj0TEGzPzO10IT+oXo62Srz5h4ihJfSwzdxrtthExQKlI/HXg4Ig4LjN/N1GxSf0oInYFPgf8Bzip5eHTgQ8B/w/4dkT8KzNPmeQQpX5glfwpyMRRkqaJamjqHyJid+CvwCGAiaO0pHcADwPbZ+Zfmh/IzOuBD0XEccDFwLsAE0epRWb+cRTbPAiMuJ16x4xuByBJmlxVgZxLgKd3ORSpFz0F+ENr0tiseuxMYKtJi0qSuszEUZKmp+uB9bodhNSDlgcWjmK7e7EdJQGlSn5V7KbT5x0cEUdPREwafx7wJGl6WomyTIekJf0deHZErDncBtWi5TsCLm8jFWcA7233QET8IiIOH+Z5z6ZNZX31JhNHSZpmqiI5mwM3dzsWqQd9B1gN+HVEPLn1wYh4InAisAZwzOSGJvW0uir5DuueAiyOI0nTz2HAY4CfdDsQqQd9C9gd2AW4IiJuAG6qHnsssCGlgXwq8NVuBChJ3WDiqF6wX0Ts1+b+wZrHJAERMXcUmw0AqwCPA14G7A88AHxj4iKT+lNmDlaVh99KWet0I0qy2PBPSsL46cwczVxISZoSTBzVC8a6+OtIi55L08H1HW4/QJnbeFBmXj0B8Uh9LzMXUdZy/FxErAesS2kz3ZKZN3Y1OEnqEhNHdduGI28iqcZoLrwsolSAvB44B/hCZl47oVFJU0Rm/pPSyyhJ05qJo7rKK7fSsslMi5xJEyAitgPmArNY8gLNDGBFYB1g18y06IekacHEUZIkqVIttXEqI1eBHMApE5KmERNHdVVE7D+KzR4C7gZuzEzXzJLGQbVG3acy86BuxyL1mPcCWwP/Bn4PPAHYDDiWskzHs4BHA38FXt2lGCVp0g0MDnqxTN0TEYvo7IrtrcAngS9nph9eqVINq3sJsCbwF+DYzLxvmG0PAj4BrJmZMycvSqn3RcTfKfPvn5SZ10fES4GfA9tl5gURsRLwQ8qSHS/KzN91MVypJ4yhPbcEz0X9wR5HdduZjHygmQnMplz1XQf4PPBkSpl0aVqLiAHKunOvre5qDJ97V0Q8PzOvadp2M+DrwDOq7W6f5HClfjAX+FNmNioWX0zZX7YDLsjMByPiAOAfwOGAiaNUWCV/ijNxVFdl5k6j3bZqID+H0vA9OCKO80qvxIHA6yiVU48DbgReCDyNMrTumQAR8T7gSGD5attvAe/pQrxSP1h8USUz50XEw8CmTffdExFnUy5iSrJK/rRg4qi+UQ1N/UO1MPNfgUPwSq/0GsrV2r0z8ziAiDgCOAHYpeplPBw4gHI1+BLg0My8sDvhSj3vn8AGLfddS7kY0+wByigYadqzSv70YBl39Z2qQM4lwNO7HIrUCzYB/t5IGmHx4uUfoySKX6QMY30IeBuwjUmjVOsMYOuI2LXpvkuBzSPi8QARsQJlyPdtkx+eJHWHPY7qV9cDT+l2EFIPWAM4q839f6m+7wBcA+yemVdOVlBSH/sMsB9wQkR8MzMPpQzt3g/4bUT8FHgBsC5wTPfClHqHVfKnBxNH9auVKAcgabqbCSxVPTUz74sIgEeAFziMSBqdzMyIeDHwVWCF6r4zI+IoSlG2xtzga4H3dydKqed8lw6K3ESEVfL7kImj+k5VJGdz4OZuxyL1gdNMGqXhVUNSr27uAcnMPwKbRsSqTfcdFhE/pgxRvQk4KTPvn/SApd5klfxpwMRR/egw4DHAT7odiNQH7up2AFKP+z/KcO+XA0TE0ZTlOL7duhZqZp5JaSBLamKV/OnBxFFdFRFzR7HZALAK8DjgZcD+lGp235i4yCRJ08TqwKym2wdQ2kff7ko00hRnlfz+ZeKobrt+5E2WMECZ23hQZl49AfFI/WiVmoswdY+RmfMmKCapX/wTeH5EvBf4R3Xf40dZ7IPMPHbCIpOmsGo+sVXy+4iJo7ptYBTbLALupSSZ5wBfyMxrJzQqqb/sWX21Gqx5rPG45wFNd0cDHwY+Ut0eBLarvkbDxFEaO6vk9xEbDOqqzHQtUWnZjeYCzHg+T5oyMvOjETEP2IZSRfVAyhI2f+xqYNL0YJX8PjIwOGgFXEmSJICIWAR8PzNHNVRV0thURXLmAf/OzKd1Ox6NzN4e9aWIWDMiLFwgSRpvzwE+PpoNI+IxExyLNJU1quSf2u1ANDoOVVVPiIjtgJcAawJ/AY5tLYPetO1BwCeqbQ+atCClHhQRfwB+l5mf6HYsUj+KiG8Bh2fmg7B4DcfRPG8f4KvAWhMYntQXrJI/PZg4qquqYQrfAl5b3TVAKUzwroh4fmZe07TtZpQ1f55RbXf7JIcr9aKdGKoEKalzBwLbRcTemfnXkTaOiNnA14B9JzwyqX9YJX8acKiquu1A4HWUZPHnwGeAK4C5NFWqi4j3ARcA21bbfhPYZLKDlSRNOT8BngxcGBFvqNswIrYHLqckjYuAj018eFJfGBjF1yDwH8o+9FXgqZn5465EqzGxOI66KiLOAp4JvCIzj6vumwGcAOwCbAEcTlmQeQC4BDg0My/sSsBSj7GQh7TsIuL1wOcpVVV/QekFuafp8ZmU5TreCcykVF3dPzPP60K4ktQV9jiq2zYB/t5IGgEys3EVdwD4ImUY60PA24BtTBolSeMpM79BGdFyNWXu1WXV3HsiYmPgPODdlKTxKODpJo2Spht7HNVVEfEIcFJm7tVy/6qU4QyDwLXA7pl5ZRdClHpa1eP4Z0ovSccy88PjG5HUvyJiFcoQuv2BBcAPgZcDKwP/Ag7MzFO6F6E0tUTEmsCnMtNih33A4jjqtpnAUtVTM/O+iAB4BHhBZt442YFJfeSp1VcnGvNNTBylSmbeDxwQEb8HjgFeXT30S8rw1X93LTipD1glf2ozcVSvO82kURrRdcDZ3Q5Cmgoi4sUsuY7jAPBs4EXAj7oSlNTjrJI/PZg4qtfd1e0ApD5wbma+duTNJA0nIlamFMg5iNKYPR04GHgT8Bbg+xGxK6VA23+6FqjUmxpV8hcBxwE3Ai8Enkapkv9MWFwl/0hg+WrbbwHv6UK8GgMTR0mSNK1FxLaUxu0TgPnA+zPzs9XDb4uI04DvAvsAz4yIV2emvfzSkNdQehj3bqqSfwRVlfyql9Eq+X3OxFG9YJWImDuGx8jMeRMUkyRpGoiIDwNHUNpEfwH2y8wrmrfJzF9HxNOA7wPPBU6PiP8FPpCZCyc7ZqkHta2SHxEfo8x5/CJlyPeDwPuAL1VV9NVHTBzVC/asvloN1jzWeNzPsCRpWbyfcj75AnBEZs5vt1Fm3hIRL6AMq/tg9f2FwDaTE6bU09YAzmpz/1+q7ztQ1j+1Sn4fcx1H9YKBMX75+ZXgj8Dfux2E1MduplTvfttwSWNDZg5m5seBHfn/7d1vqJ71fcfx91lqxZai5IEOB9Lh4LsUmrUbVFSW+XdI1BpqgxKlWTpXlj1Y2YTMdlDJZHE6ZHMrCAvUxkbcHtiYUhDakcZZCTS0oHTLPq5GDfFBLNjJCnZTc/bguiPBnnMn5yTn/l33fb1fEBLu63fB59HJ/Tm/6/r+4AjwO5MIKE2BRafkj/55Ykq+pXGKuVujppJY/qQzkOTq1hmkKbd2qcdsJDlQVZ8A/mllIkkzxyn5M8DiKEkzYHSI8o3AxcBR4Okki04lrqoNdF+YPcdRg7bcsxmTvAncdpbjSLPKKfkzwN0eNVVV+6rKMczSGaiqjXRnOX6d7vy5x4AjVfXlMbfdCty78ukkSdIscMdRrV1FtzsiaRmq6jLgcbr3S/YCL9IN67gKuG90feOp3t2S1Kmq43Tny30syYuLrCngP4D5JH6XkjpOyZ9x/rCTpOl2D11pvDPJEyc+rKpr6I4OuAnYU1W3JHmnUUZp2sydpTXSkGzAKfkzzUdVJWm6XQk8f3JpBEiyD7iCbvLjDcCuBtmkaXQ13VmN43ZAjpy0TlLHKfkzznYvSdPtfBY+O4skr1TVdcBzwO1V9VqSbRNNJ02ZJM+cxpq36I7CkYRT8ofC4qg+WFtVX1nOjU6ElHgdqMUuJnmpqm4GvgfcPSqPD08snSRJmgkWR/XBx0d/lmKO7pl4i6OGbj+wqaq2JnlkoQVJDlbVJuBJ4KGqOhffz5IAqKp1wLEkWeJ9fwRcnuTzK5NMmh5VtQ/4bpL7W2fRyrE4qg8OA99vHUKaUjvojtb4alXdRnd+4wPvX5TkW1X1BWAncD/w9mRjSr21H/gGsPn9F6rqm8D+JP+wwH3rgE2AxVFySv4gWBzVBweSbGkdQppGSQ5V1Xq64TfrgPOAXyqOo7WPVtXPgK8BF9Dt2ktafAd+A/DzCeaQpN7yRVZJmnJJ9gOX0k1PHfuYUJKngDXAg8APVjqbJEmaDe44StIUq6p3gd1JNgPfOZ17khyjO/9RkiTptFgcJWm6nTgHS5KklpySP+MsjmrtGeBQ6xCSJEk6I07Jn3EWRzWV5OrWGSRJknTGnJI/4yyO6oWqWg3cCFxMN8756SRvjFm/AVjrow2SJEm94JT8GWdxVHNVtZHubLmPnPTxW1W1I8mORW67le78LIujBNePDl9eqvkk1571NJIkaeZYHNVUVV0GPA6sAvYCLwKfojtI9r7R9Y1J/q9ZSKn/LgQuWsZ9nuMode6oqjsW+Hx+zDVJGhSLo1q7h6403pnkiRMfVtU1wG7gJmBPVd2S5J1GGaW+O0C3ay9peZY7mdhfvkgaDIujWrsSeP7k0giQZF9VXQF8j+5Q812Av/GVFnY4ya7WIaQp9eutA0gzwCn5A2BxVGvnA88udCHJK1V1HfAccHtVvZZk20TTSZJmWpJXW2eQpp1T8ofB4qjWXgdqsYtJXqqqm+l2Hu8elceHJ5ZOkiRJp8Up+bPN4qjW9gObqmprkkcWWpDkYFVtAp4EHqqqc1n++yiSJL2nqj53Gst+Afw38GqSrGwiaTo5JX/2zc3P+1632qmqNcAPgXPpHll9OskDi6zdQvcDaQ54GzgnyapJZZX6qKruBV5Isqd1FmkaVdVxljbk5hjwN8A/JvFLlMR7U/KfZeEp+fPAt1lgSn5VfQPY5Pe56eCOo5pKcqiq1tMNv1kHnAcsWByTPFpVPwO+BlyA0+wkkmxvnUGacv/Gqf8/WUW3i3Ip8KvA3wEfA/54ZaNJU8Mp+QPgjqN6oao+AFwDfCjJU6dYexHwZ8DvJbl8AvEkSaKq5oCrgUeA3wBuSPLdtqmk9qrqdeBokt9e4NpH6WZVXAL8c5I7TrrmjuMUccdRTVXVu8DuJJuB75zOPUmO0f1mS5KkiRk9mrqvqj4N/DvwBcDiKDklfxB+pXUADd4cDrqRJE2R0YCcHwGfaBxF6otTTskHbgbeopuS/8VJBdPZY3GUJElaupfpjhyQ1E3JX1NVWxdbkOQg3QTV43RT8rfh5sFUsThKkiQt3Xl0x3RIgh3A/wJfrar9VfUXCy1K8i26R7wB7gc+O6F8OgssjpIkSUswGpLzSeC11lmkPkhyCFgPHKWbkv+ZMWsfpSuMbwIfnEhAnRUOx1EfXF9V+5Zx33ySa896GkmSxvsT4NeAf2kdROqLJPur6lJGU/JPsfapqjrAaEr+JPLpzHkch5o66eDl5TzjPu/4ZknSmaiqS05j2RzwYeCjwK3A5+gey/tkkv9auXTSdHjflHzNKHcc1QcHgJ2tQ0iSBunlJa6fo3u38S5Lo/Qep+QPgMVRfXA4ya7WISRJg3Q6X3aPA/9DVzKfA/5+dLyAJA2GxVGSJA1WEgcFStJp8IelJEmSJGksdxwlSZKWqKpWAw8muat1FqknnJI/4yyOam078ELrEJKkYauqy4EbgdXAj4HHkvx8kbV30R1evhqwOEqdC4GLlnGfRzxMCYujmkqyvXUGSdJwVdUc3WTvLaOP5ui+yG6rquuS/OSktb8FPAJcNlr30wnHlfrMKfkzzuIoSZKG7A+Bz9NNTn0SeBX4fWAt8BhwBUBV/SXwFeCc0dqdwJca5JX6yin5M87iKEmShmwz3Q7jbUmeBKiqe4C9wPrRLuOfAn9At8v4I2BrkoNt4kpSG05VlSRJQ/abwKETpREgyXHgr+mK4sN0j7H+Avhz4FOWRklD5I6jJEkasguAZxf4/Mejv38X+Anw6ST/OalQktQ37jhKkqQhWwX80vTUkyaqvg1cb2mUxtoO7GkdQivLHUdJkqTF/WuSV1uHkPrMKfnD4I6jJEnS4t5oHUCS+sDiKEmSJEkay0dVJUnS0H24qi5ZxjWSHFmhTJLUK3Pz8/OtM0iSJDVRVcfpznFcjvkk/hJe0iD4w06SJA3d3ITvk6Sp446jJEmSJGksh+NIkiRJksayOEqSpMGqqn1V9aXWOSSp73zHUZIkDdlVwNHWISSp79xxlCRJkiSNZXGUJEmSJI1lcZQkSZIkjeVxHJIkabCq6jjwAvDN5dyf5K/ObiJJ6ieH40iSpKH7+OjPUswB84DFUdIgWBwlSdLQHQa+3zqEJPWZxVGSJA3dgSRbWoeQpD5zOI4kSZIkaSyLoyRJkiRpLIujJEmSJGksi6MkSRqyZ4BDrUNIUt95jqMkSZIkaSynqkqSpMGrqtXAjcDFwFHg6SRvjFm/AVibxHMcJQ2Cj6pKkqRBq6qNdGc5fh3YATwGHKmqL4+57Vbg3pVPJ0n9YHGUJEmDVVWXAY8DHwH2An9L997jh4D7qmpvVX2wYURJ6gUfVZUkSUN2D7AKuDPJEyc+rKprgN3ATcCeqrolyTuNMkpSc+44SpKkIbsSeP7k0giQZB9wBXAEuAHY1SCbJPWGxVGSJA3Z+cDLC11I8gpwHfBT4PaqenCCuSSpVyyOkiRpyF4HarGLSV4CbgbeAu6uqi9OKpgk9YnFUZIkDdl+YE1VbV1sQZKDwCbgOPBQVW0D5iYTT5L6YW5+fr51BkmSpCaqag3wQ+Bc4Fm68xsfWGTtFmAnXWl8GzgnyapJZZWkltxxlCRJg5XkELAeOAqsAz4zZu2jwGeBNwGP6JA0KBZHSZI0aEn2A5fSTU+9/xRrnwLWAA8CP1jpbJLUFz6qKkmSBquq3gV2J9ncOosk9Zk7jpIkacjmcNCNJJ2SxVGSJEmSNJbFUZIkSZI0lsVRkiRJkjSWw3EkSdJgVdVx4BhwaBm3zye59ixHkqRe+kDrAJIkSY1dCFy0jPv87bukwbA4SpKkoTsA7GwdQpL6zOIoSZKG7nCSXa1DSFKfORxHkiRJkjSWxVGSJEmSNJbFUZIkSZI0lu84SpKkIdsOvNA6hCT1nec4SpIkSZLG8lFVSZIkSdJYFkdJkiRJ0lgWR0mSJEnSWBZHSZIkSdJYFkdJkiRJ0lj/D6+K559nMLMZAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Define figure, axes, and plot\n",
    "fig, ax = plt.subplots(figsize=(15, 10))\n",
    "    \n",
    "# Error bars for 95% confidence interval\n",
    "# Can increase capsize to add whiskers\n",
    "coef_df.plot(x='variables', y='coef', kind='bar',\n",
    "            ax=ax, color='none', fontsize=22, \n",
    "            ecolor='steelblue',capsize=0,\n",
    "            yerr='errors', legend=False)\n",
    "    \n",
    "# Set title & labels\n",
    "plt.title('Coefficients of Features w/ 95% Confidence Intervals',fontsize=30)\n",
    "ax.set_ylabel('Coefficients',fontsize=22)\n",
    "ax.set_xlabel('',fontsize=22)\n",
    "    \n",
    "# Coefficients\n",
    "ax.scatter(x=pd.np.arange(coef_df.shape[0]), \n",
    "         marker='o', s=80, \n",
    "         y=coef_df['coef'], color='steelblue')\n",
    "    \n",
    "# Line to define zero on the y-axis\n",
    "ax.axhline(y=0, linestyle='--', color='red', linewidth=1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c586381c",
   "metadata": {},
   "source": [
    "##### This graphic shows four interactive coefficients ($T2TRUE:Xfemale$, $T2TRUE:Xblack$, $T2TRUE:Xfactor(dep)1$, $T2TRUE:Xagelt35$) of the second treated group with $95%$ confidence intervals. These are the following inferences:\n",
    "\n",
    "- First of all, the $T2TRUE:Xfemale$ coefficient of the second treated group, says that woman is $65.9\\%$ less likely to be unemployed for one more week compared to a man. Additionally, the cero number lies in its confidence interval, so this coefficient is not significative. \n",
    "\n",
    "- Secondly, the $T2TRUE:Xblack$ coefficient of the second treated group, says that an African American person is $45.26\\%$ more likely to be unemployed for one more week compared to a white person. Additionally, the cero number lies in its confidence interval, so this coefficient is not significative. \n",
    "\n",
    "- Thirdly, the $T2TRUE:Xfactor(dep)1$ coefficient of the second treated group, a person with only one dependent is $76.36\\%$ more likely to be unemployed for one more week compared to a person with two or more dependents. Additionally, the cero number lies in its confidence interval, so this coefficient is not significative. \n",
    "\n",
    "- Finally, the $T2TRUE:Xagelt35$ coefficient of the second treated group, a person who is younger than 35 years of age is $112\\%$ more likely to be unemployed for one more week compared to a person older than 35 years. Additionally, the cero number lies in its confidence interval, so this coefficient is not significative. In few words, all these coefficiences are no significative"
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