Regression Models for Data Science in R
A companion book for the Coursera Regression Models class
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.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
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
Related Books
Python is one of the leading open source platforms for data science and numerical computing. IPython and the associated Jupyter Notebook offer efficient interfaces to Python for data analysis and interactive visualization, and they constitute an ideal gateway to the platform.
IPython Interactive Computing and Visualization Cookbook, 2nd Edition ...
Learn how to use R to turn raw data into insight, knowledge, and understanding. This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience, R for Data Science is designed to get you doing data...
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 sur...
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 ...