Regression Analysis By Example Using R 6th Edition

Description

By (author) Hadi Ali S.; By (author) Chatterjee, Samprit

:

Regression Analysis By Example Using R

A STRAIGHTFORWARD AND CONCISE DISCUSSION OF THE ESSENTIALS OF REGRESSION ANALYSIS

In the newly revised sixth edition of Regression Analysis By Example Using R, distinguished statistician Dr Ali S. Hadi delivers an expanded and thoroughly updated discussion of exploratory data analysis using regression analysis in R. The book provides in-depth treatments of regression diagnostics, transformation, multicollinearity, logistic regression, and robust regression.

The author clearly demonstrates effective methods of regression analysis with examples that contain the types of data irregularities commonly encountered in the real world. This newest edition also offers a brand-new, easy to read chapter on the freely available statistical software package R.

Readers will also find:

• Reorganized, expanded, and upgraded exercises at the end of each chapter with an emphasis on data analysis
• Updated data sets and examples throughout the book
• Complimentary access to a companion website that provides data sets in xlsx, csv, and txt format

Perfect for upper-level undergraduate or beginning graduate students in statistics, mathematics, biostatistics, and computer science programs, Regression Analysis By Example Using R will also benefit readers who need a reference for quick updates on regression methods and applications.

Table of contents:

Preface xiv

1 Introduction 1

1.1 What Is Regression Analysis? 1

1.2 Publicly Available Data Sets 2

1.3 Selected Applications of Regression Analysis 3

1.3.1 Agricultural Sciences 3

1.3.2 Industrial and Labor Relations 4

1.3.3 Government 5

1.3.4 History 5

1.3.5 Environmental Sciences 6

1.3.6 Industrial Production 6

1.3.7 The Space Shuttle Challenger 7

1.3.8 Cost of Health Care 7

1.4 Steps in Regression Analysis 7

1.4.1 Statement of the Problem 9

1.4.2 Selection of Potentially Relevant Variables 9

1.4.3 Data Collection 9

1.4.4 Model Specification 10

1.4.5 Method of Fitting 12

1.4.6 Model Fitting 13

1.4.7 Model Criticism and Selection 14

1.4.8 Objectives of Regression Analysis 15

1.5 Scope and Organization of the Book 16

2 A Brief Introduction to R 19

2.1 What Is R and RStudio? 19

2.2 Installing R and RStudio 20

2.3 Getting Started With R 21

2.3.1 Command Level Prompt 21

2.3.2 Calculations Using R 22

2.3.3 Editing Your R Code 24

2.3.4 Best Practice: Object Names in R 25

2.4 Data Values and Objects in R 25

2.4.1 Types of Data Values in R 25

2.4.2 Types (Structures) of Objects in R 28

2.4.3 Object Attributes 34

2.4.4 Testing (Checking) Object Type 34

2.4.5 Changing Object Type 34

2.5 R Packages (Libraries) 35

2.5.1 Installing R Packages 35

2.5.2 Name Spaces 36

2.5.3 Updating R 37

2.5.4 Datasets in R Packages 37

2.6 Importing (Reading) Data into R Workspace 37

2.6.1 Best Practice: Working Directory 38

2.6.2 Reading ASCII (Text) Files 38

2.6.3 Reading CSV Files 40

2.6.4 Reading Excel Files 40

2.6.5 Reading Files from the Internet 41

2.7 Writing (Exporting) Data to Files 42

2.7.1 Diverting Normal R Output to a File 42

2.7.2 Saving Graphs in Files 42

2.7.3 Exporting Data to Files 43

2.8 Some Arithmetic and Other Operators 43

2.8.1 Vectors 43

2.8.2 Matrix Computations 45

2.9 Programming in R 50

2.9.1 Best Practice: Script Files 50

2.9.2 Some Useful Commands or Functions 50

2.9.3 Conditional Execution 51

2.9.4 Loops 53

2.9.5 Functions and Function