Julian James Faraway
（2004年刊行，Chapman and Hall / CRC［Texts in Statistical Science Series 63］， ISBN:1584884258→著者サイト）
【目次】
Contents
Preface ix
1 Introduction 1
1.1 Before You Start 1
1.2 Initial Data Analysis 2
1.3 When to Use Regression Analysis 6
1.4 History 72 Estimation 11
2.1 Linear Model 11
2.2 Matrix Representation 12
2.3 Estimating b 12
2.4 Least Squares Estimation 13
2.5 Examples of Calculating ・ 14
2.6 Gauss邦arkov Theorem 15
2.7 Goodness of Fit 16
2.8 Example 18
2.9 Identifiability 213 Inference 25
3.1 Hypothesis Tests To Compare Models 25
3.2 Testing Examples 27
3.3 Permutation Tests 32
3.4 Confidence Intervals for b 34
3.5 Confidence Intervals for Predictions 36
3.6 Designed Experiments 39
3.7 Observational Data 43
3.8 Practical Difficulties 474 Diagnostics 53
4.1 Checking Error Assumptions 53
4.2 Finding Unusual Observations 64
4.3 Checking the Structure of the Model 715 Problems with the Predictors 75
5.1 Errors in the Predictors 75
5.2 Changes of Scale 79
5.3 Collinearity 816 Problems with the Error 87
6.1 Generalized Least Squares 87
6.2 Weighted Least Squares 90
6.3 Testing for Lack of Fit 92
6.4 Robust Regression 967 Transformation 107
7.1 Transforming the Response 107
7.2 Transforming the Predictors 1108 Variable Selection 119
8.1 Hierarchical Models 119
8.2 Testing-Based Procedures 120
8.3 Criterion-Based Procedures 123
8.4 Summary 1289 Shrinkage Methods 131
9.1 Principal Components 131
9.2 Partial Least Squares 138
9.3 Ridge Regression 14110 Statistical Strategy and Model Uncertainty 145
10.1 Strategy 145
10.2 An Experiment in Model Building 146
10.3 Discussion 14711 Insurance Redlining . A Complete Example 149
11.1 Ecological Correlation 149
11.2 Initial Data Analysis 151
11.3 Initial Model and Diagnostics 154
11.4 Transformation and Variable Selection 156
11.5 Discussion 15912 Missing Data 161
13 Analysis of Covariance 165
13.1 A Two-Level Example 166
13.2 Coding Qualitative Predictors 170
13.3 A Multi-Level Factor Example 17214 One-Way Analysis of Variance 179
14.1 The Model 179
14.2 An Example 180
14.3 Diagnostics 183
14.4 Pairwise Comparisons 18415 Factorial Designs 187
15.1 Two-Way ANOVA 187
15.2 Two-Way ANOVA with One Observation per Cell 188
15.3 Two-Way ANOVA with More than One Observation per Cell 191
15.4 Larger Factorial Experiments 19516 Block Designs 201
16.1 Randomized Block Design 202
16.2 Latin Squares 206
16.3 Balanced Incomplete Block Design 210A R Installation, Functions and Data 215
B Quick Introduction to R 217
B.1 Reading the Data In 217
B.2 Numerical Summaries 217
B.3 Graphical Summaries 218
B.4 Selecting Subsets of the Data 219
B.5 Learning More about R 220
Bibliography 221
Index 225
