Table of contents for Applied regression and multilevel/hierarchical models / Andrew Gelman, Jennifer Hill.
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1. Why?; 2. Concepts and methods from basic probability and statistics; Part IA. Single-level Regression: 3. Linear regression: the basics; 4. Linear regression: before and after fitting the model; 5. Logistic regression; 6. Generalized linear models; Part IB. Working with Regression Inferences: 7. Simulation of probability models and statistical inferences; 8. Simulation for checking statistical procedures and model fits; 9. Causal inference using regression on the treatment variable; 10. Causal inference using more advanced models; Part IIA. Multilevel Regression: 11. Multilevel structures; 12. Multilevel linear models: the basics; 13. Multilevel linear models: varying slopes, non-nested models, and other complexities; 14. Multilevel logistic regression; 15. Multilevel generalized linear models; Part IIB. Fitting Multilevel Models: 16. Multilevel modeling in bugs and R: the basics; 17. Fitting multilevel linear and generalized linear models in bugs and R; 18. Likelihood and Bayesian inference and computation; 19. Debugging and speeding convergence; Part III. From Data Collection to Model Understanding to Model Checking: 20. Sample size and power calculations; 21. Understanding and summarizing the fitted models; 22. Analysis of variance; 23. Causal inference using multilevel models; 24. Model checking and comparison; 25. Missing data imputation; Appendixes: A. Six quick tips to improve your regression modeling; B. Statistical graphics for research and presentation; C. Software; References.
Library of Congress subject headings for this publication:
Regression analysis.
Multilevel models (Statistics)