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Handbook of Regression Analysis (2013) using R.pdf
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How to Use This Book
This book is designed to be a practical guide to regression modeling. There is
little theory here, and methodology appears in the service of the ultimate goal
of analyzing real data using appropriate regression tools. As such, the target
audience of the book includes anyone who is faced with regression data [that
is, data where there is a response variable that is being modeled as a function
of other variable (s)], and whose goal is to learn as much as possible from that
data.
The book can be used as a text for an applied regression course (indeed,
much of it is based on handouts that have been given to students in such a
course), but that is not its primary purpose; rather, it is aimed much more
broadly as a source of practical advice on how to address the problems that
come up when dealing with regression data. While a text is usually organized
in a way that makes the chapters interdependent, successively building on
each other, that is not the case here. Indeed, we encourage readers to dip into
different chapters for practical advice on specific topics as needed. The pace
of the book is faster than might typically be the case for a text. The coverage,
while at an applied level, does not shy away from sophisticated concepts. It is
distinct from, for example, Chatterjee and Hadi (2012), while also having less
theoretical focus than texts such as Greene (2011), Montgomery et al. (2012),
or Sen and Srivastava (1990).
This, however, is not a cookbook that presents a mechanical approach to
doing regression analysis. Data analysis is perhaps an art, and certainly a craft;
we believe that the goal of any data analysis book should be to help analysts
develop the skills and experience necessary to adjust to the inevitable twists
and turns that come up when analyzing real data.
We assume that the reader possesses a nodding acquaintance with regression
analysis. The reader should be familiar with the basic terminology
and should have been exposed to basic regression techniques and concepts,
at least at the level of simple (one-predictor) linear regression. We also
assume that the user has access to a computer with an adequate regression
package. The material presented here is not tied to any particular software.
Almost all of the analyses described here can be performed by most standard
packages, although the ease of doing this could vary. All of the analyses
presented here were done using the free package R (R Development Core
Team, 2011), which is available for many different operating system platforms
(see h t t p : //www.R-pro j e c t . org/ for more information). Code for the output and figures in the book can be found at its associated web site
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