This book contains the exercise solutions for the book R for Data Science, by Hadley Wickham and Garret Grolemund (Wickham and Grolemund 2017).
Hierarchical linear modeling (HLM) is an ordinary least square (OLS) regression-based analysis that takes the hierarchical structure of the data into account. Hierarchically structured data is nested data where groups of units are clustered together in an organized fashion, such as students within classrooms. The Lasso is a linear model that estimates sparse coefficients. It is useful in some contexts due to its tendency to prefer solutions with fewer non-zero coefficients, effectively reducing the number of features upon which the given solution is dependent.
R for Data Science itself is available online at r4ds.had.co.nz, and physical copy is published by O’Reilly Media and available from amazon.
Acknowledgments
These solutions have benefited from many contributors.A special thanks to:
- Garrett Grolemund and Hadley Wickham for writing the truly fantastic R for Data Science, without whom these solutions would not exist—literally.
- @dongzhuoer and @cfgauss for careful readings of the book and noticing numerous issues and proposing fixes.
Linear Models In Statistics Rencher Solution Manual Transmission
Thank you to all of those who contributed issues or pull-requests onGitHub(in alphabetical order): @adamblake, @benherbertson, @bhishanpdl, @bob100000000000, @carajoos, @chrisyeh96, @clemonsa, @daczarne, @dcgreaves, @decoursin, @dependabot[bot], @dongzhuoer, @dvanic, @edavishydro, @eric-k-zhu, @GoldbergData, @gvwilson, @henrikmidtiby, @ihagerman, @JamesCuster, @jdblischak, @jhoeting, @jlbeaudry, @jmclawson, @kxchia1, @liuminzhao, @lopierra, @martinruhle, @matthewlock91, @mgeard, @mjones01, @mroviras, @mugpeng, @mvhone, @neander09, @nickcorona, @nielsenmarkus11, @nzxwang, @qichun-dai, @r2ressler, @RandallEW, @rbjanis, @ricardosasso, @Shurakai, @TheMksConnection, @timothydobbins, @tinhb92, @vzei, @xiaoouwang, @xinrui112, and@zidraThank you to all of you who contributed annotations on hypothes.is (in alphabetical order): @electricdinosaurs, and @inkish.
For another set of solutions for and notes on R for Data Science see Yet Another ‘R for Data Science’ Study Guide by Bryan Shalloway.
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