Linear regression models : applications in R / John P. Hoffman.

Includes bibliographical references and index.

Introduction -- Review of elementary statistical concepts -- Simple linear regression models -- Multiple linear regression models -- The ANOVA table and goodness-of-fit statistics -- Comparing linear regression models -- Indicator variables in linear regression models -- Independence -- Homoscedasticity -- Collinearity and multicollinearity -- Normality, linearity, and interaction effects -- Models specification -- Measurement errors -- Influential observations: leverage points and outliers -- Multilevel linear regression models -- A brief introduction to logistic regression -- Conclusions.

"Research in the social and behavioral sciences has benefited from linear regression models (LRMs) for decades to identify and understand the associations among a set of explanatory variables and an outcome variable. Linear Regression Models: Applications in R provides you with a comprehensive treatment of these models and indispensable guidance about how to estimate them using the R software environment. After furnishing some background material, the author explains how to estimate simple and multiple LRMs in R, including how interpret their coefficients and understand their assumptions. Several chapters thoroughly describe these assumptions, and explain how to determine whether they are satisfied and how to modify the regression model if they are not. The book also includes chapters on specifying the correct model, adjusting for measurement error, understanding the effects of influential observations, and using the model with multilevel data. The concluding chapter presents an alternative model - logistic regression - designed for binary or two-category outcome variables. The book includes appendices that discuss data management and missing data, and provides simulations in R to test model assumptions"-- Provided by publisher.