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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.
Inhaltsverzeichnis
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 Functionals 54
2.9.6 User Defined Functions 55
2.10 Bibliographic Notes 60
3 Simple Linear Regression 65
3.1 Introduction 65
3.2 Covariance and Correlation Coefficient 65
3.3 Example: Computer Repair Data 69
3.4 The Simple Linear Regression Model 72
3.5 Parameter Estimation 73
3.6 Tests of Hypotheses 77
3.7 Confidence Intervals 82
3.8 Predictions 83
3.9 Measuring the Quality of Fit 84
3.10 Regression Line Through the Origin 88
3.11 Trivial Regression Models 89
3.12 Bibliographic Notes 90
4 Multiple Linear Regression 97
4.1 Introduction 97
4.2 Description of the Data and Model 97
4.3 Example: Supervisor Performance Data 98
4.4 Parameter Estimation 100
4.5 Interpretations of Regression Coefficients 101
4.6 Centering and Scaling 104
4.6.1 Centering and Scaling in Intercept Models 104
4.6.2 Scaling in No-Intercept Models 105
4.7 Properties of the Least Squares Estimators 106
4.8 Multiple Correlation Coefficient
