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  • Book Overview & Buying R for Data Science
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R for Data Science

R for Data Science

By : Toomey
3.4 (5)
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R for Data Science

R for Data Science

3.4 (5)
By: Toomey

Overview of this book

If you are a data analyst who has a firm grip on some advanced data analysis techniques and wants to learn how to leverage the features of R, this is the book for you. You should have some basic knowledge of the R language and should know about some data science topics.
Table of Contents (14 chapters)
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Questions

Factual

  • What is the best way to handle NA values when performing a regression?
  • When will the quantiles graph for a regression model not look like a nice line of fit?
  • Can you compare the anova versus manova results? Aside from the multiple sections, is there really a difference in the calculations?

When, how, and why?

  • Why does the Residuals vs Leverage graph show such a blob of data?
  • Why do we use 4 as a rounding number in the robust regression?
  • At what point will you feel comfortable deciding that the dataset you are using for a regression has the right set of predictors in use?

Challenges

  • Are there better predictors available for obesity than those used in the chapter?
  • How can multilevel regression be used for either the obesity or mpg datasets?
  • Can you determine a different set of predictors for mpg that does not reduce it to simple government fiat?
CONTINUE READING
83
Tech Concepts
36
Programming languages
73
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R for Data Science
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