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雷达卡
2005最新出版
Preface xiii
1 Scatterplots and Regression 1
1.1 Scatterplots, 1
1.2 Mean Functions, 9
1.3 Variance Functions, 11
1.4 Summary Graph, 11
1.5 Tools for Looking at Scatterplots, 12
1.5.1 Size, 13
1.5.2 Transformations, 14
1.5.3 Smoothers for the Mean Function, 14
1.6 Scatterplot Matrices, 15
Problems, 17
2 Simple Linear Regression 19
2.1 Ordinary Least Squares Estimation, 21
2.2 Least Squares Criterion, 23
2.3 Estimating σ 2,25
2.4 Properties of Least Squares Estimates, 26
2.5 Estimated Variances, 27
2.6 Comparing Models: The Analysis of Variance, 28
2.6.1 The F -Test for Regression, 30
2.6.2 Interpreting p-values, 31
2.6.3 Power of Tests, 31
2.7 The Coefficient of Determination, R2,31
2.8 Confidence Intervals and Tests, 32
2.8.1 The Intercept, 32
2.8.2 Slope, 33
2.8.3 Prediction, 34
2.8.4 Fitted Values, 35
2.9 The Residuals, 36
Problems, 38
3 Multiple Regression 47
3.1 Adding a Term to a Simple Linear Regression Model, 47
3.1.1 Explaining Variability, 49
3.1.2 Added-Variable Plots, 49
3.2 The Multiple Linear Regression Model, 50
3.3 Terms and Predictors, 51
3.4 Ordinary Least Squares, 54
3.4.1 Data and Matrix Notation, 54
3.4.2 Variance-Covariance Matrix of e,56
3.4.3 Ordinary Least Squares Estimators, 56
3.4.4 Properties of the Estimates, 57
3.4.5 Simple Regression in Matrix Terms, 58
3.5 The Analysis of Variance, 61
3.5.1 The Coefficient of Determination, 62
3.5.2 Hypotheses Concerning One of the Terms, 62
3.5.3 Relationship to the t-Statistic, 63
3.5.4 t-Tests and Added-Variable Plots, 63
3.5.5 Other Tests of Hypotheses, 64
3.5.6 Sequential Analysis of Variance Tables, 64
3.6 Predictions and Fitted Values, 65
Problems, 65
4 Drawing Conclusions 69
4.1 Understanding Parameter Estimates, 69
4.1.1 Rate of Change, 69
4.1.2 Signs of Estimates, 70
4.1.3 Interpretation Depends on Other Terms in the Mean Function, 70
4.1.4 Rank Deficient and Over-Parameterized Mean
Functions, 73
4.1.5 Tests, 74
4.1.6 Dropping Terms, 74
4.1.7 Logarithms, 76
4.2 Experimentation Versus Observation, 77 4.3 Sampling from a Normal Population, 80
4.4 More on R2,81
4.4.1 Simple Linear Regression and R2,83
4.4.2 Multiple Linear Regression, 84
4.4.3 Regression through the Origin, 84
4.5 Missing Data, 84
4.5.1 Missing at Random, 84
4.5.2 Alternatives, 85
4.6 Computationally Intensive Methods, 87
4.6.1 Regression Inference without Normality, 87
4.6.2 Nonlinear Functions of Parameters, 89
4.6.3 Predictors Measured with Error, 90
Problems, 92
5 Weights, Lack of Fit, and More 96
5.1 Weighted Least Squares, 96
5.1.1 Applications of Weighted Least Squares, 98
5.1.2 Additional Comments, 99
5.2 Testing for Lack of Fit, Variance Known, 100
5.3 Testing for Lack of Fit, Variance Unknown, 102
5.4 General F Testing, 105
5.4.1 Non-null Distributions, 107
5.4.2 Additional Comments, 108
5.5 Joint Confidence Regions, 108
Problems, 110
6 Polynomials and Factors 115
6.1 Polynomial Regression, 115
6.1.1 Polynomials with Several Predictors, 117
6.1.2 Using the Delta Method to Estimate a Minimum or a Maximum, 120
6.1.3 Fractional Polynomials, 122
6.2 Factors, 122
6.2.1 No Other Predictors, 123
6.2.2 Adding a Predictor: Comparing Regression Lines, 126
6.2.3 Additional Comments, 129
6.3 Many Factors, 130
6.4 Partial One-Dimensional Mean Functions, 131
6.5 Random Coefficient Models, 134
Problems, 137
7 Transformations 147
7.1 Transformations and Scatterplots, 147
7.1.1 Power Transformations, 148
7.1.2 Transforming Only the Predictor Variable, 150
7.1.3 Transforming the Response Only, 152
7.1.4 The Box and Cox Method, 153
7.2 Transformations and Scatterplot Matrices, 153
7.2.1 The 1D Estimation Result and Linearly Related Predictors, 156
7.2.2 Automatic Choice of Transformation of Predictors, 157
7.3 Transforming the Response, 159
7.4 Transformations of Nonpositive Variables, 160
Problems, 161
8 Regression Diagnostics: Residuals 167
8.1 The Residuals, 167
8.1.1 Difference Between eˆand e, 168
8.1.2 The Hat Matrix, 169
8.1.3 Residuals and the Hat Matrix with Weights, 170
8.1.4 The Residuals When the Model Is Correct, 171
8.1.5 The Residuals When the Model Is Not Correct, 171
8.1.6 Fuel Consumption Data, 173
8.2 Testing for Curvature, 176
8.3 Nonconstant Variance, 177
8.3.1 Variance Stabilizing Transformations, 179
8.3.2 A Diagnostic for Nonconstant Variance, 180
8.3.3 Additional Comments, 185
8.4 Graphs for Model Assessment, 185
8.4.1 Checking Mean Functions, 186
8.4.2 Checking Variance Functions, 189
Problems, 191
9 Outliers and Influence 194
9.1 Outliers, 194
9.1.1 An Outlier Test, 194
9.1.2 Weighted Least Squares, 196
9.1.3 Significance Levels for the Outlier Test, 196
9.1.4 Additional Comments, 197
9.2 Influence of Cases, 198
9.2.1 Cook’s Distance, 198 9.2.2 Magnitude of Di , 199
9.2.3 Computing Di , 200
9.2.4 Other Measures of Influence, 203
9.3 Normality Assumption, 204
Problems, 206
10 Variable Selection 211
10.1 The Active Terms, 211
10.1.1 Collinearity, 214
10.1.2 Collinearity and Variances, 216
10.2 Variable Selection, 217
10.2.1 Information Criteria, 217
10.2.2 Computationally Intensive Criteria, 220
10.2.3 Using Subject-Matter Knowledge, 220
10.3 Computational Methods, 221
10.3.1 Subset Selection Overstates Significance, 225
10.4 Windmills, 226
10.4.1 Six Mean Functions, 226
10.4.2 A Computationally Intensive Approach, 228
Problems, 230
11 Nonlinear Regression 233
11.1 Estimation for Nonlinear Mean Functions, 234
11.2 Inference Assuming Large Samples, 237
11.3 Bootstrap Inference, 244
11.4 References, 248
Problems, 248
12 Logistic Regression 251
12.1 Binomial Regression, 253
12.1.1 Mean Functions for Binomial Regression, 254
12.2 Fitting Logistic Regression, 255
12.2.1 One-Predictor Example, 255
12.2.2 Many Terms, 256
12.2.3 Deviance, 260
12.2.4 Goodness-of-Fit Tests, 261
12.3 Binomial Random Variables, 263
12.3.1 Maximum Likelihood Estimation, 263
12.3.2 The Log-Likelihood for Logistic Regression, 264
12.4 Generalized Linear Models, 265
Problems, 266
Appendix 270
A.1 Web Site, 270
A.2 Means and Variances of Random Variables, 270
A.2.1 E Notation, 270
A.2.2 Var Notation, 271
A.2.3 Cov Notation, 271
A.2.4 Conditional Moments, 272
A.3 Least Squares for Simple Regression, 273
A.4 Means and Variances of Least Squares Estimates, 273
A.5 Estimating E(Y |X) Using a Smoother, 275
A.6 A Brief Introduction to Matrices and Vectors, 278
A.6.1 Addition and Subtraction, 279
A.6.2 Multiplication by a Scalar, 280
A.6.3 Matrix Multiplication, 280
A.6.4 Transpose of a Matrix, 281
A.6.5 Inverse of a Matrix, 281
A.6.6 Orthogonality, 282
A.6.7 Linear Dependence and Rank of a Matrix, 283
A.7 Random Vectors, 283
A.8 Least Squares Using Matrices, 284
A.8.1 Properties of Estimates, 285
A.8.2 The Residual Sum of Squares, 285
A.8.3 Estimate of Variance, 286
A.9 The QR Factorization, 286
A.10 Maximum Likelihood Estimates, 287
A.11 The Box-Cox Method for Transformations, 289
A.11.1 Univariate Case, 289
A.11.2 Multivariate Case, 290
A.12 Case Deletion in Linear Regression, 291
References 293
Author Index 301
Subject Index 305
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