목차
List of examples xvii
Preface xix
1 Why? 1
2 Concepts and methods from basic probability and statistics 13
Part 1A: Single-level regression 29
3 Linear regression: the basics 31
4 Linear regression: before and after fitting the model 53
5 Logistic regression 79
6 Generalized linear models 109
Part 1B: Working with regression inferences 135
7 Simulation of probability models and statistical inferences 137
8 Simulation for checking statistical procedures and model fits 155
9 Causal inferece using regression on the treatment variable 167
10 Causal inferece using more advanced models 199
Part 2A: Multilevel regression 235
11 Multilevel structures 237
12 Multilevel linear models: the basics 251
13 Multilevel linear models: varying slopes, non-nested models, and other complexities 279
14 Multilevel logistic regression 301
15 Multilevel generalized linear models 325
Part 2B: Fitting multilevel models 343
16 Multilevel modeling in Bugs and R: the basic 345
17 Fitting multilevel linear and generalized linear models in Bugs and R 375
18 Likelihood and Bayesian inference and computation 387
19 Debugging and speeding convergence 415
Part 3: From data collection to model understanding to model checking 435
20 Sample size and power calculations 437
21 Understanding and summarizing the fitted models 457
22 Analysis of variance 487
23 Causal inference using multilevel models 503
24 Model checking and comparison 513
25 Missing-data imputation 529
Appendixes 545
A Six quick tips to improve your regression modeling 547
B Statistical graphics for research and presentation 551
C Software 565
Refereces 575
Author index 601
Subject index 607