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:	54
Created:	2022-02-03
License:	CC BY-NC-SA
Author(s):	Brian Caffo

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