Summary and Info
John Fox introduces readers to the techniques of kernel estimation, additive nonparametric regression, and the ways nonparametric regression can be employed to select transformations of the data preceding a linear least-squares fit "Data Analysis Using Regression and Multilevel/Hierarchical Models is a comprehensive manual for the applied researcher who wants to perform data analysis using linear and nonlinear regression and multilevel models. The book introduces a wide variety of models, whilst at the same time instructing the reader in how to fit these models using available software packages. The book illustrates the concepts by working through scores of real data examples that have arisen from the authors' own applied research, with programming codes provided for each one. Topics covered include causal inference, including regression, poststratification, matching, regression discontinuity, and instrumental variables, as well as multilevel logistic regression and missing-data imputation. Practical tips regarding building, fitting, and understanding are provided throughout."--Publisher description. Read more... Why? -- Concepts and methods from basic probability and statistics -- Linear regression: the basics -- Linear regression: before and after fitting the model -- Logistic regression -- Generalized linear models -- Simulation for checking statistical procedures and model fits -- Causal inference using regression on the treatment variable -- Causal inference using more advanced models -- Multilevel structures -- Multilevel linear models: the basics -- Multilevel linear models: varying slopes, non-nested models, and other complexities. Multilevel logistic regression -- Multilevel generalized linear models -- Multilevel modeling Bugs and R: the basics -- Fitting multilevel linear and generalized linear models in Bugs and R -- Likelihood and Bayesian inference and computation -- Debugging and speeding convergence -- Sample size and power calculations -- Understanding and summarizing the fitted models -- Analysis of variance -- Causal inference using multilevel models -- Model checking and comparison -- Missing-data imputation -- Six quick tips to improve your regression modeling -- Statistical graphics for research and presentation -- Software
More About the Author
Andrew Gelman (born February 11, 1965) is an American statistician, professor of statistics and political science, and director of the Applied Statistics Center at Columbia University.
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