Data Analysis Using Regression and Multilevel/Hierarchical Models, first published in 2007, is a comprehensive manual for the applied researcher who wants to perform data analysis using linear and nonlinear regression and multilevel models. It introduces and demonstrates a variety of models and instructs the reader in how to fit these models using freely available software packages.
Data Analysis Using Regression and Multilevel/Hierarchical Models, first published in 2007, is a comprehensive manual for the applied researcher who wants to perform data analysis using linear and nonlinear regression and multilevel models. It introduces and demonstrates a variety of models and instructs the reader in how to fit these models using freely available software packages.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
Andrew Gelman is Professor of Statistics and Professor of Political Science at Columbia University. He has published over 150 articles in statistical theory, methods, and computation, and in applications areas including decision analysis, survey sampling, political science, public health, and policy. His other books are Bayesian Data Analysis (1995, second edition 2003) and Teaching Statistics: A Bag of Tricks (2002).
Inhaltsangabe
1. Why? 2. Concepts and methods from basic probability and statistics Part I. A. 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 I. B. 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 II. A. 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 II. B. 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.
1. Why? 2. Concepts and methods from basic probability and statistics Part I. A. 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 I. B. 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 II. A. 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 II. B. 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.
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