Praise for the Fourth Edition:
"This book is . . . an excellent source of examples for
regression analysis. It has been and still is readily readable and
understandable."
--Journal of the American Statistical
Association Regression analysis is a conceptually simple
method for investigating relationships among variables. Carrying
out a successful application of regression analysis, however,
requires a balance of theoretical results, empirical rules, and
subjective judgment. Regression Analysis by Example, Fifth
Edition has been expanded and thoroughly updated to reflect
recent advances in the field. The emphasis continues to be on
exploratory data analysis rather than statistical theory. The book
offers in-depth treatment of regression diagnostics,
transformation, multicollinearity, logistic regression, and robust
regression.
The book now includes a new chapter on the detection and
correction of multicollinearity, while also showcasing the use of
the discussed methods on newly added data sets from the fields of
engineering, medicine, and business. The Fifth Edition also
explores additional topics, including:
* Surrogate ridge regression
* Fitting nonlinear models
* Errors in variables
* ANOVA for designed experiments
Methods of regression analysis are clearly demonstrated, and
examples containing the types of irregularities commonly
encountered in the real world are provided. Each example isolates
one or two techniques and features detailed discussions, the
required assumptions, and the evaluated success of each technique.
Additionally, methods described throughout the book can be carried
out with most of the currently available statistical software
packages, such as the software package R.
Regression Analysis by Example, Fifth Edition is suitable
for anyone with an understanding of elementary statistics.
Autorentext
SAMPRIT CHATTERJEE, PhD, is Professor Emeritus of
Statistics at New York University. A Fellow of the American
Statistical Association, Dr. Chatterjee has been a Fulbright
scholar in both Kazhakstan and Mongolia. He is the coauthor of
Sensitivity Analysis in Linear Regression and A Casebook
for a First Course in Statistics and Data Analysis, both
published by Wiley.
ALI S. HADI, PhD, is a Distinguished University Professor
and former vice provost at the American University in Cairo (AUC).
He is the founding Director of the Actuarial Science Program at
AUC. He is also a Stephen H. Weiss Presidential Fellow and
Professor Emeritus at Cornell University. Dr. Hadi is the author of
four other books, a Fellow of the American Statistical Association,
and an elected Member of the International Statistical
Institute.
Zusammenfassung
Praise for the Fourth Edition:
"This book is . . . an excellent source of examples for regression analysis. It has been and still is readily readable and understandable."
Journal of the American Statistical Association Regression analysis is a conceptually simple method for investigating relationships among variables. Carrying out a successful application of regression analysis, however, requires a balance of theoretical results, empirical rules, and subjective judgment. Regression Analysis by Example, Fifth Edition has been expanded and thoroughly updated to reflect recent advances in the field. The emphasis continues to be on exploratory data analysis rather than statistical theory. The book offers in-depth treatment of regression diagnostics, transformation, multicollinearity, logistic regression, and robust regression.
The book now includes a new chapter on the detection and correction of multicollinearity, while also showcasing the use of the discussed methods on newly added data sets from the fields of engineering, medicine, and business. The Fifth Edition also explores additional topics, including:
- Surrogate ridge regression
- Fitting nonlinear models
- Errors in variables
- ANOVA for designed experiments
Methods of regression analysis are clearly demonstrated, and examples containing the types of irregularities commonly encountered in the real world are provided. Each example isolates one or two techniques and features detailed discussions, the required assumptions, and the evaluated success of each technique. Additionally, methods described throughout the book can be carried out with most of the currently available statistical software packages, such as the software package R.
Regression Analysis by Example, Fifth Edition is suitable for anyone with an understanding of elementary statistics.
Inhalt
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
1.4 Steps in Regression Analysis 13
1.5 Scope and Organization of the Book 21
Exercises 23
2 Simple Linear Regression 25
2.1 Introduction 25
2.2 Covariance and Correlation Coefficient 25
2.3 Example: Computer Repair Data 30
2.4 The Simple Linear Regression Model 32
2.5 Parameter Estimation 33
2.6 Tests of Hypotheses 36
2.7 Confidence Intervals 41
2.8 Predictions 41
2.9 Measuring the Quality of Fit 43
2.10 Regression Line Through the Origin 46
2.11 Trivial Regression Models 48
2.12 Bibliographic Notes 49
Exercises 49
3 Multiple Linear Regression 57
3.1 Introduction 57
3.2 Description of the Data and Model 57
3.3 Example: Supervisor Performance Data 58
3.4 Parameter Estimation 61
3.5 Interpretations of Regression Coefficients 62
3.6 Centering and Scaling 64
3.7 Properties of the Least Squares Estimators 67
3.8 Multiple Correlation Coefficient 68
3.9 Inference for Individual Regression Coefficients 69
3.10 Tests of Hypotheses in a Linear Model 71
3.11 Predictions 81
3.12 Summary 82
Exercises 82
Appendix: Multiple Regression in Matrix Notation 89
4 Regression Diagnostics: Detection of Model Violations 93
4.1 Introduction 93
4.2 The Standard Regression Assumptions 94
4.3 Various Types of Residuals 96
4.4 Graphical Methods 98
4.5 Graphs Before Fitting a Model 101
4.6 Graphs After Fitting a Model 105
4.7 Checking Linearity and Normality Assumptions 105
4.8 Leverage, Influence, and Outliers 106
4.9 Measures of Influence 111
4.10 The Potential-Residual Plot 115
4.11 What to Do with the Outliers? 116
4.12 Role of Variables in a Regression Equation 117
4.13 Effects of an Additional Predictor 122
4.14 Robust Regression 123
Exercises 123
5 Qualitative Variables as Predictors 129
5.1 Introduction 129
5.2 Salary Survey Data 130
5.3 Interaction Variables 133
5.4 Systems of Regression Equations 136
5.5 Other Applications of Indicator Variables 147
5.6 Seasonality 148
5.7 Stability of Regression Parameters Over Time 149
Exercises 151
6 Transformation of Variables 163
6.1 Introduction 163
6.2 Transformations to Achieve Linearity 165
6.3 Bacteria Deaths Due to XRay Radiation 167
6.4 Transformations to Stabilize Variance 171
6.5 Detection of Heteroscedastic Errors 176
6.6 Removal of Heteroscedasticity 178
6.7 Weighted Least Squares 179
6.8 Logarithmic Transformation of Data 180
6.9 Power Transformation 181
6.10 Summary 185
Exercises 186
7 Weighted Le…