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R for Data Science by Dan Toomey

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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 ...

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