Statistical Models for Causal Analysis

Simplifies the treatment of statistical inference focusing on how to specify and interpret models in the context of testing causal theories. Simple bivariate regression, multiple regression, multiple classification analysis, path analysis, logit regression, multinomial logit regression and survival models are among the subjects covered. Features an appendix of computer programs (for major statistical packages) that are used to generate illustrative examples contained in the chapters. Bivariate Linear Regression.

Multiple Regression.

Multiple Classification Analysis.

Path Analysis.

Logit Regression.

Multinomial Logit Regression.

Survival Models 1: Life Tables.

Survival Models 2: Proportional Hazard Models.

Survival Models 3: Hazard Models with Time Dependence.

Appendices.

References.

Index.

Robert D. Retherford and Minja Kim Choe are the authors of Statistical Models for Causal Analysis, published by Wiley.