Regression analysis by example:
Gespeichert in:
| Beteiligte Personen: | , |
|---|---|
| Format: | Buch |
| Sprache: | Englisch |
| Veröffentlicht: |
Hoboken, NJ
Wiley
2012
|
| Ausgabe: | 5. ed. |
| Schriftenreihe: | Wiley series in probability and statistics
|
| Schlagwörter: | |
| Links: | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=025322502&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| Beschreibung: | Includes index |
| Umfang: | XV, 393 S. graph. Darst. |
| ISBN: | 9780470905845 |
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| 245 | 1 | 0 | |a Regression analysis by example |c Samprit Chatterjee ; Ali S. Hadi |
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Datensatz im Suchindex
| _version_ | 1819278476985761792 |
|---|---|
| adam_text | Titel: Regression analysis by example
Autor: Chatterjee, Samprit
Jahr: 2012
CONTENTS
Preface xiii
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 6
1.3.4 History 6
1.3.5 Environmental Sciences 9
1.3.6 Industrial Production 9
1.3.7 The Space Shuttle Challenger 12
1.3.8 Costof Health Care 12
1.4 Steps in Regression Analysis 13
1.4.1 Statement of the Problem 13
1.4.2 Selection of Potentially Relevant Variables 15
1.4.3 Data Collection 15
1.4.4 Model Specification 16
1.4.5 MethodofFitting 19
1.4.6 Model Fitting 19
1.4.7 Model Criticism and Selection 19
1.4.8 Objectives of Regression Analysis 20
vii
VIII CONTENTS
1.5 Scope and Organization of the Book 21
Exercises 23
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 Bibliographie Notes 49
Exercises 49
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 59
3.5 Interpretations of Regression Coefficients 62
3.6 Centering and Scaling 64
3.6.1 Centering and Scaling in Intercept Models 65
3.6.2 Scaling in No-Intercept Models 66
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
3.10.1 Testing All Regression Coefficients Equal to Zero 73
3.10.2 Testing a Subset of Regression Coefficients Equal to
Zero 75
3.10.3 Testing the Equality of Regression Coefficients 78
3.10.4 Estimating and Testing of Regression Parameters
Under Constraints 79
3.11 Predictions 81
3.12 Summary 82
Exercises 82
Appendix: Multiple Regression in Matrix Notation 89
CONTENTS IX
Regression Diagnostics: Detection of Model Violations 93
4.1 Introduction 93
4.2 The Standard Regression Assumptions 94
4.3 Various Types of Residuais 96
4.4 Graphical Methods 98
4.5 Graphs Before Fitting a Model 101
4.5.1 One-Dimensional Graphs 101
4.5.2 Two-Dimensional Graphs 101
4.5.3 Rotating Plots 104
4.5.4 Dynamic Graphs 104
4.6 Graphs After Fitting a Model 105
4.7 Checking Linearity and Normality Assumptions 105
4.8 Leverage, Influence, and Outliers 106
4.8.1 Outliers in the Response Variable 108
4.8.2 Outliers in the Predictors 108
4.8.3 Masking and Swamping Problems 108
4.9 Measures of Influence 111
4.9.1 Cook s Distance 111
4.9.2 Welsch and Kuh Measure 112
4.9.3 Hadi s Influence Measure 113
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
4.12.1 Added-Variable Plot 117
4.12.2 Residual Plus Component Plot 118
4.13 Effects of an Additional Predictor 121
4.14 Robust Regression 123
Exercises 123
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 137
5.4.1 Models with Different Slopes and Different Intercepts 138
5.4.2 Models with Same Slope and Different Intercepts 145
5.4.3 Models with Same Intercept and Different Slopes 146
5.5 Other Applications of Indicator Variables 147
5.6 Seasonality 148
5.7 Stability of Regression Parameters Over Time 150
Exercises 154
X CONTENTS
6 Transformation of Variables 163
6.1 Introduction 163
6.2 Transformations to Achieve Linearity 165
6.3 Bacteria Deaths Due to X-Ray Radiation 167
6.3.1 Inadequacy of a Linear Model 168
6.3.2 Logarithmic Transformation for Achieving Linearity 170
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 Least Squares 191
7.1 Introduction 191
7.2 Heteroscedastic Models 192
7.2.1 Supervisors Data 192
7.2.2 College Expense Data 194
7.3 Two-Stage Estimation 195
7.4 Education Expenditure Data 197
7.5 Fitting a Dose-Response Relationship Curve 206
Exercises 208
8 The Problem of Correlated Errors 209
8.1 Introduction: Autocorrelation 209
8.2 Consumer Expenditure and Money Stock 210
8.3 Durbin-Watson Statistic 212
8.4 Removal of Autocorrelation by Transformation 214
8.5 Iterative Estimation with Autocorrelated Errors 216
8.6 Autocorrelation and Missing Variables 217
8.7 Analysis of Housing Starts 218
8.8 Limitations of the Durbin-Watson Statistic 222
8.9 Indicator Variables to Remove Seasonality 223
8.10 Regressing Two Time Series 226
Exercises 228
9 Analysis of Collinear Data 233
9.1 Introduction 233
9.2 Effects of Collinearity on Inference 234
9.3 Effects of Collinearity on Forecasting 240
CONTENTS Xi
9.4 Detection of Collinearity 245
9.4.1 Simple Signs of Collinearity 245
9.4.2 Variance Inflation Factors 248
9.4.3 The Condition Indices 251
Exercises 255
10 Working With Collinear Data 259
10.1 Introduction 259
10.2 Principal Components 259
10.3 Computations Using Principal Components 263
10.4 Imposing Constraints 265
10.5 Searching for Linear Functions of the /3 s 268
10.6 Biased Estimation of Regression Coefficients 271
10.7 Principal Components Regression 272
10.8 Reduction of Collinearity in the Estimation Data 274
10.9 Constraints on the Regression Coefficients 276
10.10 Principal Components Regression: A Caution 277
10.11 Ridge Regression 279
10.12 Estimation by the Ridge Method 281
10.13 Ridge Regression: Some Remarks 286
10.14 Summary 287
10.15 Bibliographie Notes 287
Exercises 288
Appendix 10.A: Principal Components 292
Appendix 10.B: Ridge Regression 294
Appendix 10.C: Surrogate Ridge Regression 296
11 Variable Selection Procedures 299
11.1 Introduction 299
11.2 Formulation of the Problem 300
11.3 Consequences of Variables Deletion 300
11.4 Uses of Regression Equations 302
11.4.1 Description and Model Building 302
11.4.2 Estimation and Prediction 302
11.4.3 Control 302
11.5 Criteria for Evaluating Equations 303
11.5.1 Residual Mean Square 303
11.5.2 Mallows Cv 304
11.5.3 Information Criteria 305
11.6 Collinearity and Variable Selection 306
11.7 Evaluating All Possible Equations 306
11.8 Variable Selection Procedures 307
11.8.1 Forward Selection Procedure 307
XII CONTENTS
11.8.2 Backward Elimination Procedure 308
11.8.3 Stepwise Method 308
11.9 General Remarks on Variable Selection Methods 309
11.10 A Study of Supervisor Performance 310
11.11 Variable Selection with Collinear Data 314
11.12 The Homicide Data 314
11.13 Variable Selection Using Ridge Regression 317
11.14 Selection of Variables in an Air Pollution Study 318
11.15 A Possible Strategy for Fitting Regression Models 326
11.16 Bibliographie Notes 328
Exercises 328
Appendix: Effects of Incorrect Model Specifications 331
12 Logistic Regression 335
12.1 Introduction 335
12.2 Modeling Qualitative Data 336
12.3 The Logit Model 336
12.4 Example: Estimating Probability of Bankruptcies 338
12.5 Logistic Regression Diagnostics 341
12.6 Determination of Variables to Retain 342
12.7 Judging the Fit of a Logistic Regression 345
12.8 The Multinomial Logit Model 347
12.8.1 Multinomial Logistic Regression 347
12.8.2 Example: Determining Chemical Diabetes 348
12.8.3 Ordinal Logistic Regression 352
12.8.4 Example: Determining Chemical Diabetes Revisited 353
12.9 Classification Problem: Another Approach 354
Exercises 355
13 Further Topics 359
13.1 Introduction 359
13.2 Generalized Linear Model 359
13.3 Poisson Regression Model 360
13.4 Introduction of New Drugs 361
13.5 Robust Regression 363
13.6 Fitting a Quadratic Model 364
13.7 Distribution of PCB in U.S. Bays 366
Exercises 370
Appendix A: Statistical Tables 371
References 381
Index 389
|
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| discipline | Politologie Soziologie Mathematik Wirtschaftswissenschaften |
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| illustrated | Illustrated |
| indexdate | 2024-12-20T16:15:46Z |
| institution | BVB |
| isbn | 9780470905845 |
| language | English |
| lccn | 2012009795 |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-025322502 |
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| physical | XV, 393 S. graph. Darst. |
| publishDate | 2012 |
| publishDateSearch | 2012 |
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| publisher | Wiley |
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| series2 | Wiley series in probability and statistics |
| spellingShingle | Chatterjee, Samprit 1938- Hadi, Ali S. Regression analysis by example Statistik (DE-588)4056995-0 gnd Regressionsanalyse (DE-588)4129903-6 gnd Anwendung (DE-588)4196864-5 gnd |
| subject_GND | (DE-588)4056995-0 (DE-588)4129903-6 (DE-588)4196864-5 (DE-588)4123623-3 |
| title | Regression analysis by example |
| title_auth | Regression analysis by example |
| title_exact_search | Regression analysis by example |
| title_full | Regression analysis by example Samprit Chatterjee ; Ali S. Hadi |
| title_fullStr | Regression analysis by example Samprit Chatterjee ; Ali S. Hadi |
| title_full_unstemmed | Regression analysis by example Samprit Chatterjee ; Ali S. Hadi |
| title_short | Regression analysis by example |
| title_sort | regression analysis by example |
| topic | Statistik (DE-588)4056995-0 gnd Regressionsanalyse (DE-588)4129903-6 gnd Anwendung (DE-588)4196864-5 gnd |
| topic_facet | Statistik Regressionsanalyse Anwendung Lehrbuch |
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