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Book Categories |
1 | Bivariate Linear Regression | 1 |
1.1 | Terminology | 1 |
1.2 | Fitting a Least-Squares Line | 2 |
1.3 | The Least-Squares Line as a Causal Model | 5 |
1.4 | The Bivariate Linear Regression Model as a Statistical Model | 7 |
1.5 | Statistical Inference: Generalizing from Sample to Underlying Population | 10 |
1.6 | Goodness of Fit | 23 |
2 | Multiple Regression | 29 |
2.1 | The Problem of Bias in Bivariate Linear Regression | 30 |
2.2 | Multiple Regression with Two Predictor Variables | 31 |
2.3 | Multiple Regression with Three or More Predictor Variables | 34 |
2.4 | Dummy Variables to Represent Categorical Variables | 34 |
2.5 | Multicollinearity | 38 |
2.6 | Interaction | 40 |
2.7 | Nonlinearities | 46 |
2.8 | Goodness of Fit | 51 |
2.9 | Statistical Inference | 55 |
2.10 | Stepwise Regression | 62 |
2.11 | Illustrative Examples | 63 |
3 | Multiple Classification Analysis | 69 |
3.1 | The Basic MCA Table | 70 |
3.2 | The MCA Table in Deviation Form | 78 |
3.3 | MCA with Interactions | 82 |
3.4 | MCA with Additional Quantitative Control Variables | 85 |
3.5 | Expressing Results from Ordinary Multiple Regression in an MCA Format (all Variables Quantitative) | 88 |
3.6 | Presenting MCA Results Graphically | 90 |
4 | Path Analysis | 93 |
4.1 | Path Diagrams and Path Coefficients | 93 |
4.2 | Path Models with More Than One Exogenous Variable | 100 |
4.3 | Path Models with Control Variables | 105 |
4.4 | Saturated and Unsaturated Path Models | 107 |
4.5 | Path Analysis with Standardized Variables | 109 |
4.6 | Path Models with Interactions and Nonlinearities | 114 |
5 | Logit Regression | 119 |
5.1 | The Linear Probability Model | 119 |
5.2 | The Logit Regression Model | 121 |
5.3 | Statistical Inference | 137 |
5.4 | Goodness of Fit | 140 |
5.5 | MCA Adapted to Logit Regression | 142 |
5.6 | Fitting the Logit Regression Model | 147 |
5.7 | Some Limitations of the Logit Regression Model | 150 |
6 | Multinomial Logit Regression | 151 |
6.1 | From Logit to Multinomial Logit | 151 |
6.2 | Multinomial Logit Models with Interactions and Nonlinearities | 159 |
6.3 | A More General Formulation of the Multinomial Logit Model | 160 |
6.4 | Reconceptualizing Contraceptive Method Choice as a Two-Step Process | 163 |
7 | Survival Models, Part 1: Life Tables | 167 |
7.1 | Actuarial Life Table | 168 |
7.2 | Product-Limit Life Table | 175 |
7.3 | The Life Table in Continuous Form | 178 |
8 | Survival Models, Part 2: Proportional Hazard Models | 181 |
8.1 | Basic Form of the Proportional Hazard Model | 182 |
8.2 | Calculation of Life Tables from the Proportional Hazard Model | 194 |
8.3 | Statistical Inference and Goodness of Fit | 198 |
8.4 | A Numerical Example | 198 |
8.5 | Multiple Classification Analysis (MCA) Adapted to Proportional Hazard Regression | 205 |
9 | Survival Models, Part 3: Hazard Models with Time Dependence | 207 |
9.1 | Time-Dependent Predictor Variables | 207 |
9.2 | Time-Dependent Coefficients | 214 |
Appendix A Sample Computer Programs | 221 | |
A.1 | Description of the FIJIDATA File | 221 |
A.2 | SAS Mainframe Programs | 223 |
A.3 | Preparing Data for Survival Analysis | 227 |
A.4 | BMDP Mainframe Programs | 230 |
A.5 | LIMDEP Programs for IBM-Compatible Personal Computers | 238 |
Appendix B Statistical Reference Tables | 245 | |
References | 251 | |
Index | 254 |
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