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Regression Analysis for Social Sciences
of: Alexander von Eye, Christof Schuster
Elsevier Trade Monographs, 1998
ISBN: 9780080550824 , 386 Pages
Format: PDF, ePUB, Read online
Copy protection: DRM
Front Cover
1
Regression Analysis for Social Sciences
4
Copyright Page
5
Contents
6
Preface
12
CHAPTER 1. INTRODUCTION
18
CHAPTER 2. SIMPLE LINEAR REGRESSION
24
2.1 Linear Functions and Estimation
24
2.2 Parameter Estimation
29
2.3 Interpreting Regression Parameters
43
2.4 Interpolation and Extrapolation
45
2.5 Testing Regression Hypotheses
46
CHAPTER 3. MULTIPLE LINEAR REGRESSION
60
3.1 Ordinary Least Squares Estimation
61
3.2 Data Example
67
3.3 Multiple Correlation and Determination
70
3.4 Significance Testing
75
CHAPTER 4. CATEGORICAL PREDICTORS
80
4.1 Dummy and Effect Coding
82
4.2 More Than Two Categories
87
4.3 Multiple Categorical Predictors
94
CHAPTER 5. OUTLIER ANALYSIS
98
5.1 Leverage Outliers
98
5.2 Remedial Measures
106
CHAPTER 6. RESIDUAL ANALYSIS
116
6.1 Illustrations of Residual Analysis
117
6.2 Residuals and Variable Relationships
123
CHAPTER 7. POLYNOMIAL REGRESSION
134
7.1 Basics
134
7.2 Orthogonal Polynomials
141
7.3 Example of Non-Equidistant Predictors
145
CHAPTER 8. MULTICOLLINEARITY
150
8.1 Diagnosing Multicollinearity
153
8.2 Countermeasures to Multicollinearity
155
CHAPTER 9. MULTIPLE CURVILINEAR REGRESSION
160
CHAPTER 10. INTERACTION TERMS IN REGRESSION
168
10.1 Definition and Illustrations
168
10.2 Multiplicative Terms
171
10.3 Variable Characteristics
179
CHAPTER 11. ROBUST REGRESSION
192
11.1 The Concept of Robustness
192
11.2 Models of Robust Regression
195
11.3 Computational Issues
208
CHAPTER 12. SYMMETRIC REGRESSION
226
12.1 Pearson’s Orthogonal Regression
227
12.2 Other Solutions
236
12.3 A General Model for OLS Regression
242
12.4 Robust Symmetrical Regression
247
12.5 Computational Issues
247
CHAPTER 13. VARIABLE SELECTION TECHNIQUES
254
13.1 A Data Example
257
13.2 Best Subset Regression
261
13.3 Stepwise Regression
268
13.4 Discussion
274
CHAPTER 14. REGRESSION FOR LONGITUDINAL DATA
276
14.1 Within Subject Correlation
277
14.2 Robust Modeling of Longitudinal Data
283
14.3 A Data Example
287
CHAPTER 15. PIECEWISE REGRESSION
294
15.1 Continuous Piecewise Regression
295
15.2 Discontinuous Piecewise Regression
298
CHAPTER 16. DICHOTOMOUS CRITERION VARIABLES
304
CHAPTER 17. COMPUTATIONAL ISSUES
308
17.1 Creating a SYSTAT System File
308
17.2 Simple Regression
312
17.3 Curvilinear Regression
315
17.4 Multiple Regression
321
17.5 Regression Interaction
325
17.6 Regression with Categorical Predictors
327
17.7 The Partial Interaction Strategy
332
17.8 Residual Analysis
336
17.9 Missing Data Estimation
340
17.10 Piecewise Regression
345
APPENDIX A. ELEMENTS OF MATRIX ALGEBRA
350
A.1 Definition of a Matrix
350
A.2 Types of Matrices
352
A.3 Transposing Matrices
354
A.4 Adding Matrices
354
A.5 Multiplying Matrices
355
A.6 The Rank of a Matrix
359
A.7 The Inverse of a Matrix
361
A.8 The Determinant of a Matrix
363
A.9 Rules for Operations with Matrices
364
A.10 Exercises
366
APPENDIX B. BASICS OF DIFFERENTIATION
368
APPENDIX C. BASICS OF VECTOR DIFFERENTIATION
372
APPENDIX D. POLYNOMIALS
376
D.1 Systems of Orthogonal Polynomials
378
D.2 Smoothing Series of Measures
380
APPENDIX E. DATA SETS
382
E.1 Recall Performance Data
382
E.2 Examination and State Anxiety Data
387
References
390
Index
398
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