Sharpen your Data Science skills with this is a hands-on workshop on advanced regression techniques in R.
In this workshop, we will talk about a variety of regression models, give their definitions, discuss goodness-of-fit criteria, present fitted models, interpret estimated regression coefficients, and use the fitted models for prediction. The models will be limited to linear regression, Box-Cox transformation, gamma regression, ordinary logistic regression, Poisson regression, beta regression, longitudinal (repeated measures) regression, and hierarchical model.
The workshop is designed to be hands-on. Participants are required to bring laptops and be ready to write R, analyze data and interpret results. For each model, we present an example with a complete R code, and then will an exercise to work on. Workshop participants should be familiar with algebraic expressions of different probability distributions, and have a fundamental knowledge of simple linear regression: normally distributed random error, continuous and categorical independent variables (requiring creating dummy variables).
The material covered by the workshop will be taken from my recently published book “Advanced Regression Models with SAS and R Applications”, CRC Press, 2018.
We will have a limited number of books for sale. You can purchase the book and get it signed by Dr. Olga.
Dr. Olga Korosteleva, is a professor of Statistics at the Department of Mathematics and Statistics at California State University, Long Beach (CSULB). She received her Bachelor’s degree in Mathematics in 1996 from Wayne State University in Detroit, and a Ph.D. in Statistics from Purdue University in West Lafayette, Indiana, in 2002. Since then she has been teaching mostly Statistics courses in the Master’s program in Applied Statistics at CSULB, and loving it!
Dr. Olga is an undergraduate advisor for students majoring in Mathematics with an option in Statistics. She is also the faculty supervisor for the Statistics Student Association. She is also the immediate past-president of the Southern California Chapter of the American Statistical Association (SCASA). Dr. Olga is the editor-in-chief of SCASA’s monthly eNewsletter and the author (co-author) of four statistical books.
When: October 1, 2022
- Saturday: 8:30 AM - 04:30 PM
Where:
University of California, Irvine -- Paul Merage School of Business
4293 Pereira Drive
Irvine, CA 92617
- Google Maps
- Directions & Parking Information
- Rooms
- SB1 2200 - Main event room
- SB1 3rd floor patio - meals
Registration
- Cost: $10
- Register through EventBright
- All participants must register for the event and have a valid ticket to attend. If there is space you can register at the door.
- All participants must abide by the SoCal RUG Code of Conduct, including the R Consortium and the R Community Code of Conduct.
- Connect to SSID: UCInet Mobile
- Go to https://oit.uci.edu/reg
- register your device as a guest
If you have problems, please call OIT support line at (949) 824-2222 option 3
SoCal RUG GitHub Repo: https://github.com/socalrug/
Please install git and clone the following repo before the event and pull before the start of the event
command:
git clone https://github.com/socalrug/advanced_regression_2022-10-01.git
Event Repo: https://github.com/socalrug/advanced_regression_2022-10-01
A slack channel has been set up for the hackathon. This will be used for general announcements but it is also a great source for you to ask questions to other participants.
If you have not created an account on our slack group, create one using the following link:
Slack Group Sign-up: https://tinyurl.com/socalrug-slack-signup
Once you have an account, sign in (you can do it on a web browser or download an app on your phone or desktop).
Slack channel: https://tinyurl.com/socalrug-slack
The channel for the course is regression-2022
Since this event depends on you have an R setup that is functional with the correct packages and version of R, we highly recommend that you run the check_setup.r before the event. If you have issues, please reach out to use in the slack channel (see above) to get help.
Please follow us on twitter, oc_rug, and also tweet about the event with the hash tag #OCRUG
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- 1-page note sheets covering data science fundamentals and useful R packages.
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- Comprehensive book on the complete data science workflow, including data importing/cleaning, visualization, and data analysis
- Focus on
tidyversepackages - Accessible for beginners who have a basic grasp of R
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- This is the hub website for the core
tidyversepackages - Check out the Packages section and associated links for helpful information on using the packages.
- This is the hub website for the core
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- This book digs into the details of R.
- A great resource for more advanced users wanting to learning more about R under the hood.
- There is also a 1st Edition of the book.
Food, drinks and snacks will be provided throughout the event. We will have vegetarian options available. Please feel free to bring any additional food for yourself if you would like to supplement the meals or if you have other specific dietary constraints.
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Saturday
- Lunch: Boxed sandwich
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Snacks and Drinks
- Coffee
- Soft drinks
- Water
- Various snacks, (e.g. fruit, chips, nuts, granola bars)
| Qty | Description |
|---|---|
| 15 | Vegan Boxed Lunch |
| 11 | Honey Mustard Ham and Swiss Sandwich |
| 11 | Roast Beef and Cheddar Sandwich |
| 11 | Deli Sliced Turkey and Swiss Sandwich |
| 11 | Grilled Chicken Club with Bacon and Swiss Sandwich |
| Start | End | Activity | Slides | Location |
|---|---|---|---|---|
| 08:30 | 09:00 | Sign-in | SB1 Lobby | |
| 09:00 | 09:30 | Introduction and computer setup | SB1 2200 | |
| 09:30 | 10:30 | Linear Regression - definition, fitted model, interpretation of estimated regression coefficients, prediction, R application | 5-18 | SB1 2200 |
| 10:30 | 11:00 | Gamma regression | 19-32 | SB1 2200 |
| 11:00 | 11:15 | Break | ||
| 11:15 | 11:40 | Logistic regression | 33-43 | SB1 2200 |
| 11:40 | 12:10 | Poisson regression | 44-52 | SB1 2200 |
| 12:10 | 12:30 | Zero-inflated Poisson regression | 53-66 | SB1 2200 |
| 12:30 | 01:30 | Lunch | Patio | |
| 01:30 | 02:00 | Beta regressions | 67-77 | SB1 2200 |
| 02:00 | 02:30 | Longitudinal normal regression | 78-86 | SB1 2200 |
| 02:30 | 02:45 | Break | ||
| 02:45 | 03:15 | Longitudinal normal regression: Exercise | 87-92 | SB1 2200 |
| 03:15 | 03:45 | Longitudinal logistic regression | 93-103 | SB1 2200 |
| 03:45 | 04:15 | Hierarchical normal models | 104-110 | SB1 2200 |
| 04:15 | 04:30 | Wrap-up | SB1 2200 |