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| 현재위치 : 홈 > 도서목록 > 새로나온 도서 |
| Data Analysis Using Regression and Multilevel/Hierarchical Models |
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| 지은이 |
: Gelman |
| 발행일 |
: 2007 년 |
| ISBN |
: 9780521686891 |
| 정행가 |
: 45,000 원 |
| 페이지 |
: 625 페이지 |
| 판행수 |
: 1판 |
| 출판사 |
: Cambridge |
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| 저자 및 역자 소개 |
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| 차례 |
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
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