http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
Many textbooks on regression focus on theory and the simplest examples. Real statistical problems, however, are complex and subtle. This is not a book about the theory of regression. It is a book about how to use regression to solve real problems of comparison, estimation, prediction and causal inference. It focuses on practical issues such as sample size and missing data and a wide range of goals and techniques. It jumps right in to methods and computer code you can use fresh out of the box.
Preface -- Part 1: Fundamentals. Overview -- Data and measurement -- Some basic methods in mathematics and probability -- Statistical inference -- Simulation -- Part 2: Linear regression -- Background on regression modeling -- Linear regression with a single predictor -- Fitting regression models -- Prediction and Bayesian inference -- Linear regression with multiple predictors -- Assumptions, diagnostics, and model evaluation -- Transformations and regression -- Part 3: Generalized linear models. Logistic regression -- Working with logistic regression -- Other generalized linear models -- Part 4: Before and after fitting a regression. Design and sample size decisions -- Poststratification and missing-data imputation -- Part 5: Causal inference -- Causal inference and randomized experiments -- Causal inference using regression on the treatment variable -- Observational studies with all confounders assumed to be measured -- Additional topics in causal inference -- Part 6: What comes next? Advanced regression and multilevel models -- Appendixes. Computing in R -- 10 quick tips to improve your regression modeling.
Andrew Gelman, Columbia University, New York, Jennifer Hill, New York University, Aki Vehtari, Aalto University, Finland.
Includes bibliographical references (pages [497]-515) and indexes.