Praise for the Third Edition
"...this is an excellent book which could easily be used as a
course text..."
--International Statistical Institute
The Fourth Edition of Applied Linear
Regression provides a thorough update of the basic theory
and methodology of linear regression modeling. Demonstrating the
practical applications of linear regression analysis techniques,
the Fourth Edition uses interesting, real-world
exercises and examples.
Stressing central concepts such as model building, understanding
parameters, assessing fit and reliability, and drawing conclusions,
the new edition illustrates how to develop estimation, confidence,
and testing procedures primarily through the use of least squares
regression. While maintaining the accessible appeal of each
previous edition,Applied Linear Regression, Fourth
Edition features:
* Graphical methods stressed in the initial exploratory phase,
analysis phase, and summarization phase of an analysis
* In-depth coverage of parameter estimates in both simple and
complex models, transformations, and regression diagnostics
* Newly added material on topics including testing, ANOVA, and
variance assumptions
* Updated methodology, such as bootstrapping, cross-validation
binomial and Poisson regression, and modern model selection
methods
Applied Linear Regression, Fourth Edition is an
excellent textbook for upper-undergraduate and graduate-level
students, as well as an appropriate reference guide for
practitioners and applied statisticians in engineering, business
administration, economics, and the social sciences.
Autorentext
SANFORD WEISBERG, PhD, is Professor of Statistics and Director of the Statistical Consulting Service in the School of Statistics at the University of Minnesota. He is also a coauthor of Applied Regression Including Computing and Graphics and An Introduction to Regression Graphics, both published by Wiley.
Klappentext
Praise for the Third Edition
"...this is an excellent book which could easily be used as a course text..."
—International Statistical Institute
The Fourth Edition of Applied Linear Regression provides a thorough update of the basic theory and methodology of linear regression modeling. Demonstrating the practical applications of linear regression analysis techniques, the Fourth Edition uses interesting, real-world exercises and examples.
Stressing central concepts such as model building, understanding parameters, assessing fit and reliability, and drawing conclusions, the new edition illustrates how to develop estimation, confidence, and testing procedures primarily through the use of least squares regression. While maintaining the accessible appeal of each previous edition, Applied Linear Regression, Fourth Edition features:
- Graphical methods stressed in the initial exploratory phase, analysis phase, and summarization phase of an analysis
- In-depth coverage of parameter estimates in both simple and complex models, transformations, and regression diagnostics
- Newly added material on topics including testing, ANOVA, and variance assumptions
- Updated methodology, such as bootstrapping, cross-validation binomial and Poisson regression, and modern model selection methods
Applied Linear Regression, Fourth Edition is an excellent textbook for upper-undergraduate and graduate-level students, as well as an appropriate reference guide for practitioners and applied statisticians in engineering, business administration, economics, and the social sciences.
Zusammenfassung
Praise for the Third Edition
"...this is an excellent book which could easily be used as a course text..."
International Statistical Institute
The Fourth Edition of Applied Linear Regression provides a thorough update of the basic theory and methodology of linear regression modeling. Demonstrating the practical applications of linear regression analysis techniques, the Fourth Edition uses interesting, real-world exercises and examples.
Stressing central concepts such as model building, understanding parameters, assessing fit and reliability, and drawing conclusions, the new edition illustrates how to develop estimation, confidence, and testing procedures primarily through the use of least squares regression. While maintaining the accessible appeal of each previous edition,Applied Linear Regression, Fourth Edition features:
- Graphical methods stressed in the initial exploratory phase, analysis phase, and summarization phase of an analysis
- In-depth coverage of parameter estimates in both simple and complex models, transformations, and regression diagnostics
- Newly added material on topics including testing, ANOVA, and variance assumptions
- Updated methodology, such as bootstrapping, cross-validation binomial and Poisson regression, and modern model selection methods
Applied Linear Regression, Fourth Edition is an excellent textbook for upper-undergraduate and graduate-level students, as well as an appropriate reference guide for practitioners and applied statisticians in engineering, business administration, economics, and the social sciences.
Inhalt
1 Scatterplots 1
1.1 Scatterplots 2
1.2 Mean Functions 9
1.3 Variance Functions 12
1.4 Summary Graph 12
1.5 Tools for Looking at Scatterplots 13
1.6 Scatterplot Matrices 15
1.7 Problems 17
2 Simple Linear Regression 21
2.1 Ordinary Least Squares Estimation 22
2.2 Least Squares Criterion 24
2.3 Estimating the Variance 2 26
2.4 Properties of Least Squares Estimates 27
2.5 Estimated Variances 28
2.6 Confidence Intervals and -Tests 29
2.7 The Coefficient of Determination, 2 33
2.8 The Residuals 35
2.9 Problems 37
3 Multiple Regression 49
3.1 Adding a Regressor to a Simple Linear Regression Model 49
3.2 The Multiple Linear Regression Model 53
3.3 Predictors and Regressors 53
3.4 Ordinary Least Squares 57
3.5 Predictions, Fitted Values and Linear Combinations 65
3.6 Problems 66
4 Interpretation of Main Effects 71
4.1 Understanding Parameter Estimates 71
4.2 Dropping Regressors 81
4.3 Experimentation Versus Observation 84
4.4 Sampling from a Normal Population 86
4.5 More on 2 88
4.6 Problems 90
5 Complex Regressors 95
5.1 Factors 95
5.2 Many Factors 105
5.3 Polynomial Regression 106
5.4 Splines 109
5.5 Principal Components 112
5.6 Missing Data 115
5.7 Problems 118
6 Testing and Analysis of Variance 129
6.1 -tests 130
6.2 The Analysis of Variance 134
6.3 Comparisons of Means 138
6.4 Power and Non-null Distributions 138
6.5 Wald Tests 140
6.6 Interpreting Tests 142
6.7 Problems 145
7 Variances 151
7.1 Weighted Least Squares 151
7.2 Misspecified Variances 157
7.3 General Correlation Structures 162
7.4 Mixed Models 163
7.5 Variance Stabilizing Transformations 165
7.6 The Delta Method 166
7.7 The Bootstrap 168
7.8 Problems 173
8 Transformations 179
8.1 Transformation Basics 179
8.2 A General Approach to Transformations 185
8.3 Transforming the Response 190
8.4 Transformations of Nonpositive Variables 192
8.5 Additive Models 192
8.6 Problems 193
9 R…