**by Brian Caffo**

DescriptionTable of ContentsDetailsHashtagsReport an issue ### 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. ### Table of Contents

### Book Details

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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).

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

Title

Regression Models for Data Science in R

Subject

Computer Science

Publisher

Leanpub

Published

2019

Pages

144

Edition

1

Language

English

PDF Size

4.3 MB

License

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