Real statistical problems are complex and subtle. This text is about using regression to solve real problems of comparison, estimation, prediction, and causal inference, based on real stories from the authors' experience. It offers practical advice for understanding assumptions and implementing methods through graphics and computing in R and Stan.
Real statistical problems are complex and subtle. This text is about using regression to solve real problems of comparison, estimation, prediction, and causal inference, based on real stories from the authors' experience. It offers practical advice for understanding assumptions and implementing methods through graphics and computing in R and Stan.
The authors are experienced researchers who have published articles in hundreds of different scientific journals in fields including statistics, computer science, policy, public health, political science, economics, sociology, and engineering. They have also published articles in the Washington Post, New York Times, Slate, and other public venues. Their previous books include Bayesian Data Analysis, Teaching Statistics: A Bag of Tricks, and Data Analysis and Regression Using Multilevel/Hierarchical Models. Andrew Gelman is Higgins Professor of Statistics and Professor of Political Science at Columbia University.
Inhaltsangabe
Preface Part I. Fundamentals: 1. Overview 2. Data and measurement 3. Some basic methods in mathematics and probability 4. Statistical inference 5. Simulation Part II. Linear Regression: 6. Background on regression modeling 7. Linear regression with a single predictor 8. Fitting regression models 9. Prediction and Bayesian inference 10. Linear regression with multiple predictors 11. Assumptions, diagnostics, and model evaluation 12. Transformations and regression Part III. Generalized Linear Models: 13. Logistic regression 14. Working with logistic regression 15. Other generalized linear models Part IV. Before and After Fitting a Regression: 16. Design and sample size decisions 17. Poststratification and missing-data imputation Part V. Causal Inference: 18. Causal inference and randomized experiments 19. Causal inference using regression on the treatment variable 20. Observational studies with all confounders assumed to be measured 21. Additional topics in causal inference Part VI. What Comes Next?: 22. Advanced regression and multilevel models Appendices: A. Computing in R B. 10 quick tips to improve your regression modelling References Author index Subject index.
Preface Part I. Fundamentals: 1. Overview 2. Data and measurement 3. Some basic methods in mathematics and probability 4. Statistical inference 5. Simulation Part II. Linear Regression: 6. Background on regression modeling 7. Linear regression with a single predictor 8. Fitting regression models 9. Prediction and Bayesian inference 10. Linear regression with multiple predictors 11. Assumptions, diagnostics, and model evaluation 12. Transformations and regression Part III. Generalized Linear Models: 13. Logistic regression 14. Working with logistic regression 15. Other generalized linear models Part IV. Before and After Fitting a Regression: 16. Design and sample size decisions 17. Poststratification and missing-data imputation Part V. Causal Inference: 18. Causal inference and randomized experiments 19. Causal inference using regression on the treatment variable 20. Observational studies with all confounders assumed to be measured 21. Additional topics in causal inference Part VI. What Comes Next?: 22. Advanced regression and multilevel models Appendices: A. Computing in R B. 10 quick tips to improve your regression modelling References Author index Subject index.
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