Book Image

R for Data Science

By : Dan Toomey
Book Image

R for Data Science

By: Dan Toomey

Overview of this book

Table of Contents (19 chapters)



  • 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?


  • 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?