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Research in 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 to 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.
Features
Furnishes a thorough introduction and detailed information about the linear
regression model, including how to understand and interpret its results, test
assumptions, and adapt the model when assumptions are not satisfied.
Uses numerous graphs in R to illustrate the model’s results, assumptions, and
other features.
Does not assume a background in calculus or linear algebra, rather, an
introductory statistics course and familiarity with elementary algebra are
sufficient.
Provides many examples using real-world datasets relevant to various academic
disciplines.
Fully integrates the R software environment in its numerous examples.
The book is aimed primarily at advanced undergraduate and graduate students in social,
behavioral, health sciences, and related disciplines, taking a first course in linear
regression. It could also be used for self-study and would make an excellent reference
for any researcher in these fields. The R code and detailed examples provided throughout
the book equip the reader with an excellent set of tools for conducting research on
numerous social and behavioral phenomena.
John P. Hoffmann is a professor of sociology at Brigham Young University where he
teaches research methods and applied statistics courses and conducts research on
substance use and criminal behavior.