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references.bib
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@article{DanischKrumbiegel2021,
doi = {10.21105/joss.03349},
url = {https://doi.org/10.21105/joss.03349},
year = {2021},
publisher = {The Open Journal},
volume = {6},
number = {65},
pages = {3349},
author = {Simon Danisch and Julius Krumbiegel},
title = {Makie.jl: Flexible high-performance data visualization for Julia},
journal = {Journal of Open Source Software}
}
@Article{LinRegOutliers,
author = {Satman et al.},
title = {LinRegOutliers: A Julia package for detecting outliers in linear regression},
journal = {Journal of Open Source Software},
year = 2021,
doi = "10.21105/joss.02892",
volume = 6,
number = 57}
@article{JSSv040i01,
title={The Split-Apply-Combine Strategy for Data Analysis},
volume={40},
url={https://www.jstatsoft.org/index.php/jss/article/view/v040i01},
doi={10.18637/jss.v040.i01},
abstract={Many data analysis problems involve the application of a split-apply-combine strategy, where you break up a big problem into manageable pieces, operate on each piece independently and then put all the pieces back together. This insight gives rise to a new R package that allows you to smoothly apply this strategy, without having to worry about the type of structure in which your data is stored. The paper includes two case studies showing how these insights make it easier to work with batting records for veteran baseball players and a large 3d array of spatio-temporal ozone measurements.},
number={1},
journal={Journal of Statistical Software},
author={Wickham, Hadley},
year={2011},
pages={1–29}
}
@Article{tidy-data,
author = {Hadley Wickham},
issue = {10},
journal = {The Journal of Statistical Software},
selected = {TRUE},
title = {Tidy data},
url = {http://www.jstatsoft.org/v59/i10/},
volume = {59},
year = {2014},
bdsk-url-1 = {http://www.jstatsoft.org/v59/i10/},
}
@article{GALTON_10.2307/2841583,
ISSN = {09595295},
URL = {http://www.jstor.org/stable/2841583},
author = {Francis Galton},
journal = {The Journal of the Anthropological Institute of Great Britain and Ireland},
pages = {246--263},
publisher = {[Royal Anthropological Institute of Great Britain and Ireland, Wiley]},
title = {Regression Towards Mediocrity in Hereditary Stature.},
urldate = {2022-12-29},
volume = {15},
year = {1886}
}
@article{CressieRead_10.2307_2345686,
ISSN = {00359246},
URL = {http://www.jstor.org/stable/2345686},
abstract = {This article investigates the family {Iλ;λ ∈ R} of power divergence statistics for testing the fit of observed frequencies {Xi;i = 1,...,k} to expected frequencies {Ei;i = 1,...,k}. From the definition 2nIλ = 2/λ(λ + 1) ∑ki = 1 Xi{(Xi/Ei)λ - 1}; λ ∈ R, it can easily be seen that Pearson's X2 (λ = 1), the log likelihood ratio statistic (λ = 0), the Freeman-Tukey statistic (λ = -1/2) the modified log likelihood ratio statistic (λ = -1) and the Neyman modified X2 (λ = -2), are all special cases. Most of the work presented is devoted to an analytic study of the asymptotic difference between different Iλ, however finite sample results have been presented as a check and a supplement to our conclusions. A new goodness-of-fit statistic, where λ = 2/3, emerges as an excellent and compromising alternative to the old warriors, I0 and I1.},
author = {Noel Cressie and Timothy R. C. Read},
journal = {Journal of the Royal Statistical Society. Series B (Methodological)},
number = {3},
pages = {440--464},
publisher = {[Royal Statistical Society, Wiley]},
title = {Multinomial Goodness-of-Fit Tests},
urldate = {2022-12-25},
volume = {46},
year = {1984}
}
@article{Box_non_normality_10.1093/biomet/40.3-4.318,
author = {BOX, G. E. P.},
title = "{NON-NORMALITY AND TESTS ON VARIANCES}",
journal = {Biometrika},
volume = {40},
number = {3-4},
pages = {318-335},
year = {1953},
month = {12},
issn = {0006-3444},
doi = {10.1093/biomet/40.3-4.318},
url = {https://doi.org/10.1093/biomet/40.3-4.318},
eprint = {https://academic.oup.com/biomet/article-pdf/40/3-4/318/492745/40-3-4-318.pdf},
}
@book{storopolihuijzeralonso2021juliadatascience,
title = {Julia Data Science},
author = {Jose Storopoli and Rik Huijzer and Lazaro Alonso},
url = {https://juliadatascience.io},
year = {2021},
isbn = {9798489859165}
}
@Book{JuliaForDataAnalysis,
author = {Bogumił Kamiński},
title = {Julia for Data Analysis },
publisher = {Manning},
year = 2022,
url = {https://www.manning.com/books/julia-for-data-analysis}}
@article{JSSv107i04,
title={DataFrames.jl: Flexible and Fast Tabular Data in Julia},
volume={107},
url={https://www.jstatsoft.org/index.php/jss/article/view/v107i04},
doi={10.18637/jss.v107.i04},
abstract={<p>DataFrames.jl is a package written for and in the Julia language offering flexible and efficient handling of tabular data sets in memory. Thanks to Juliaâs unique strengths, it provides an appealing set of features: Rich support for standard data processing tasks and excellent flexibility and efficiency for more advanced and non-standard operations. We present the fundamental design of the package and how it compares with implementations of data frames in other languages, its main features, performance, and possible extensions. We conclude with a practical illustration of typical data processing operations.</p>},
number={4},
journal={Journal of Statistical Software},
author={Bouchet-Valat, Milan and Kamiński, Bogumił},
year={2023},
pages={1â32}
}
@book{nazarathy2021statisticsjulia,
title={Statistics with Julia: Fundamentals for Data Science,
Machine Learning and Artificial Intelligence},
author={Nazarathy, Yoni and Klok, Hayden},
year={2021},
publisher={Springer}
}
@Book{WackerlyMendenhallSchaeffer,
author = {Dennis Wackerly and William Mendenhall and Richard L. Scheaffer},
title = {Mathematical Statistics with Applications},
publisher = {Cengage},
year = 2008,
edition = {7th}}
@Book{Faraway,
author = {Julian J. Faraway},
title = {Linear Models with R},
publisher = {hapman & Hall/CRC},
year = 2004}
@Book{Embrace-Uncertainty-Fitting-Mixed-Effects-Models-with-Julia,
author = {Phillip Alday and Reinhold Kliegl and Douglas Bates},
title = {Embrace Uncertainty: Fitting Mixed-Effects Models with Julia},
publisher = {https://juliamixedmodels.github.io/EmbraceUncertainty/},
year = 2022}
@article{bezanson2017julia,
title={Julia: A fresh approach to numerical computing},
author={Bezanson, Jeff and Edelman, Alan and Karpinski, Stefan and Shah, Viral B},
journal={SIAM review},
volume={59},
number={1},
pages={65--98},
year={2017},
publisher={SIAM},
url={https://doi.org/10.1137/141000671}
}
@article{DanischKrumbiegel2021,
doi = {10.21105/joss.03349},
url = {https://doi.org/10.21105/joss.03349},
year = {2021},
publisher = {The Open Journal},
volume = {6},
number = {65},
pages = {3349},
author = {Simon Danisch and Julius Krumbiegel},
title = {Makie.jl: Flexible high-performance data visualization for Julia},
journal = {Journal of Open Source Software}
}
@software{GLM,
author = {D. Bates and others},
title = {GLM.jl},
url = {https://github.com/JuliaStats/GLM.jl},
doi = {10.5281}
}
@software{AoG,
author = {Pietro Vertechi and others},
title = {AlgebraOfGraphics.jl},
url = {https://github.com/MakieOrg/AlgebraOfGraphics.jl}
}