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R for Data Science

You're reading from   R for Data Science Learn and explore the fundamentals of data science with R

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Product type Paperback
Published in Dec 2014
Last Updated in Feb 2025
Publisher
ISBN-13 9781784390860
Length 364 pages
Edition 1st Edition
Languages
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Author (1):
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 Toomey Toomey
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Toomey
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Toc

Table of Contents (14) Chapters Close

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?
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83
Tech Concepts
36
Programming languages
73
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