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  • Book Overview & Buying Data Analysis with R, Second Edition
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Data Analysis with R, Second Edition

Data Analysis with R, Second Edition - Second Edition

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Data Analysis with R, Second Edition

Data Analysis with R, Second Edition

3.5 (2)

Overview of this book

Frequently the tool of choice for academics, R has spread deep into the private sector and can be found in the production pipelines at some of the most advanced and successful enterprises. The power and domain-specificity of R allows the user to express complex analytics easily, quickly, and succinctly. Starting with the basics of R and statistical reasoning, this book dives into advanced predictive analytics, showing how to apply those techniques to real-world data though with real-world examples. Packed with engaging problems and exercises, this book begins with a review of R and its syntax with packages like Rcpp, ggplot2, and dplyr. From there, get to grips with the fundamentals of applied statistics and build on this knowledge to perform sophisticated and powerful analytics. Solve the difficulties relating to performing data analysis in practice and find solutions to working with messy data, large data, communicating results, and facilitating reproducibility. This book is engineered to be an invaluable resource through many stages of anyone’s career as a data analyst.
Table of Contents (19 chapters)
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Linear regression diagnostics


I would be negligent if I failed to mention the boring but very critical topic of the assumptions of linear models, and how to detect violations of those assumptions. Just like the assumptions of the hypothesis tests in Chapter 6, Testing Hypotheses, linear regression has its own set of assumptions, the violation of which jeopardizes the accuracy of our model and any inferences derived from it to varying degrees. The checks and tests that ensure these assumptions are met are called diagnostics.

There are five major assumptions of linear regression:

  • That the errors (residuals) are normally distributed with a mean of zero
  • That the error terms are uncorrelated
  • That the errors have a constant variance
  • That the effect of the independent variables on the dependent variable are linear and additive
  • That multi-collinearity is at a minimum

We'll briefly touch on these assumptions, and how to check for them in this section here. To do this, we will be using a residual-fitted...

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Data Analysis with R, Second Edition
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