Regression Models for Data Science in R

Download free course Regression Models for Data Science in R, pdf file on 144 pages by Brian Caffo.

The ideal reader for this book will be quantitatively literate and has a basic understanding of statistical concepts and R programming.

 
 
The student should have a basic understanding of statistical inference such as contained in "Statistical inference for data science". The book gives a rigorous treatment of the elementary concepts of regression models from a practical perspective.
 
 
After reading the book and watching the associated videos, students will be able to perform multivariable regression models and understand their interpretations.


Table of contents

  • Introduction
  • Notation
  • Ordinary least squares
  • Regression to the mean
  • Statistical linear regression models
  • Residuals
  • Regression inference
  • Multivariable regression analysis
  • Multivariable examples and tricks
  • Adjustment
  • Residuals, variation, diagnostics
  • Multiple variables and model selection
  • Generalized Linear Models
  • Binary GLMs
  • Count data
Pages : 144
Size : 4.3 MB
File type : PDF
Downloads: 51
Created: 2022-02-03
License: CC BY-NC-SA
Author(s): Brian Caffo
Regression Models for Data Science in R

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