Regression and Other Stories
-10%
portes grátis
Regression and Other Stories
Hill, Jennifer; Gelman, Andrew; Vehtari, Aki
Cambridge University Press
07/2020
548
Mole
Inglês
9781107676510
15 a 20 dias
1060
Descrição não disponível.
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.
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
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.
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
