Applied Regression Analysis, Linear
Models, and Related Methods by Dr.
John Fox
Useful And Understandable
An accessible, detailed, and up-to-date treatment of regression analysis,
linear models, and closely related methods is provided in this book.
Incorporating nearly 200 graphs and numerous examples and exercises
that employ real data from the social sciences, the book begins with a
consideration of the role of statistical data analysis in social research. It
then moves on to cover the following topics: graphical methods for
examining and transforming data; linear least-squares regression; dummy-
variables regression; analysis of variance; diagnostic methods for
discovering whether a linear model fit to data adequately represents the
data; extensions to linear least squares, including logit and probit models,
time-series regression, nonlinear

Personal Review: Applied Regression Analysis, Linear Models,
and Related Methods by Dr. John Fox
This book takes an unusual start. It begin with the assumption that
regression usually has to the data and illustrates how the assumption can
be violated, illustrates why graphical analysis is important for data analysis
and, in chapter 4, explains how to "fix" the violations of the requirement to
the data, before actually starting to explain regression models. I find this
unusual approach very insightful. Moreover, difficult parts are marked with
an asterisk and can be left out if this is more convenient for the reader.
Although some math is required, I find this book very understandable
throughout due to its focus on application. The book covers linear models
and some extensions (for the large part of the book) and also Logit- and
Probit models for nominal data (in Chapter 15). Chapter 16 deals with
bootstrapping, and the appendices give some introduction to statistical and
mathematical requirements that the book poses.
Overall, a good buy for people who apply regressions (as the title says),
probably not so much for those who are in statistics, math, or econ.
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