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Practical Guide to Logistic Regression (Hardcover)
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Practical Guide to Logistic Regression (Hardcover)
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Practical Guide to Logistic Regression covers the key points of the
basic logistic regression model and illustrates how to use it
properly to model a binary response variable. This powerful
methodology can be used to analyze data from various fields,
including medical and health outcomes research, business analytics
and data science, ecology, fisheries, astronomy, transportation,
insurance, economics, recreation, and sports. By harnessing the
capabilities of the logistic model, analysts can better understand
their data, make appropriate predictions and classifications, and
determine the odds of one value of a predictor compared to another.
Drawing on his many years of teaching logistic regression, using
logistic-based models in research, and writing about the subject,
Professor Hilbe focuses on the most important features of the
logistic model. Serving as a guide between the author and readers,
the book explains how to construct a logistic model, interpret
coefficients and odds ratios, predict probabilities and their
standard errors based on the model, and evaluate the model as to
its fit. Using a variety of real data examples, mostly from health
outcomes, the author offers a basic step-by-step guide to
developing and interpreting observation and grouped logistic models
as well as penalized and exact logistic regression. He also gives a
step-by-step guide to modeling Bayesian logistic regression. R
statistical software is used throughout the book to display the
statistical models while SAS and Stata codes for all examples are
included at the end of each chapter. The example code can be
adapted to readers own analyses. All the code is available on the
author's website.
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