Regression Models for Data Science in R

A companion book for the Coursera Regression Models class

by Brian Caffo

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Book Description

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.

This open book is licensed under a Creative Commons License (CC BY-NC-SA). You can download Regression Models for Data Science in R ebook for free in PDF format (4.3 MB).

Table of Contents

Chapter 1
Introduction
 
Chapter 2
Notation
 
Chapter 3
Ordinary least squares
 
Chapter 4
Regression to the mean
 
Chapter 5
Statistical linear regression models
 
Chapter 6
Residuals
 
Chapter 7
Regression inference
 
Chapter 8
Multivariable regression analysis
 
Chapter 9
Multivariable examples and tricks
 
Chapter 10
Adjustment
 
Chapter 11
Residuals, variation, diagnostics
 
Chapter 12
Multiple variables and model selection
 
Chapter 13
Generalized Linear Models
 
Chapter 14
Binary GLMs
 
Chapter 15
Count data
 

Book Details

Subject
Computer Science
Publisher
Leanpub
Published
2019
Pages
144
Edition
1
Language
English
PDF Size
4.3 MB
License
CC BY-NC-SA

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