**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

### Related Books

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

Subject

Computer Science

Publisher

Leanpub

Published

2019

Pages

144

Edition

1

Language

English

PDF Size

4.3 MB

License

This engaging and clearly written textbook/reference provides a must-have introduction to the rapidly emerging interdisciplinary field of data science. It focuses on the principles fundamental to becoming a good data scientist and the key skills needed to build systems for collecting, analyzing, and interpreting data. The Data Science Design Manual...

Just how strong is the movement of big data analytics to the cloud? This report, based on a survey by O'Reilly data analyst John King, reveals that the desire among corporations to adopt big data-as-a-service is gaining momentum-and that many organizations with big data cloud experience are likely to expand their use.
King's recent survey of IT ...

The demand for skilled data science practitioners in industry, academia, and government is rapidly growing. This book introduces concepts and skills that can help you tackle real-world data analysis challenges. It covers concepts from probability, statistical inference, linear regression and machine learning. It also helps you develop skills such a...

R is one of the most popular, powerful data analytics languages and environments in use by data scientists. Actionable business data is often stored in Relational Database Management Systems (RDBMS), and one of the most widely used RDBMS is Microsoft SQL Server. Much more than a database server, it's a rich ecostructure with advanced analytic capab...

Today we are witnessing an increased use of data visualization in society. Across domains such as work, education and the news, various forms of graphs, charts and maps are used to explain, convince and tell stories. In an era in which more and more data are produced and circulated digitally, and digital tools make visualization production increasi...

This book describes methods of mortality forecasting and discusses possible improvements. It contains a selection of previously unpublished and published papers, which together provide a state-of-the-art overview of statistical approaches as well as behavioural and biological perspectives. The different parts of the book provide discussions of cu...