本書特色： ●Emphasis on practice rather than theory sets this apart from other texts ●Three chapters on causal inference ●Code and data for all examples in the book are available on the web site in the popular open-source programs R and Stan 本書目錄： 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 Appendixes A - Computing in R B - 10 quick tips to improve your regression modeling