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Statistical Learning from a Regression Perspective (Paperback, Softcover reprint of the original 2nd ed. 2016)
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Statistical Learning from a Regression Perspective (Paperback, Softcover reprint of the original 2nd ed. 2016)
Series: Springer Texts in Statistics
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This textbook considers statistical learning applications when
interest centers on the conditional distribution of the response
variable, given a set of predictors, and when it is important to
characterize how the predictors are related to the response. This
fully revised new edition includes important developments over the
past 8 years. Consistent with modern data analytics, it emphasizes
that a proper statistical learning data analysis derives from sound
data collection, intelligent data management, appropriate
statistical procedures, and an accessible interpretation of
results. As in the first edition, a unifying theme is supervised
learning that can be treated as a form of regression analysis. Key
concepts and procedures are illustrated with real applications,
especially those with practical implications. The material is
written for upper undergraduate level and graduate students in the
social and life sciences and for researchers who want to apply
statistical learning procedures to scientific and policy problems.
The author uses this book in a course on modern regression for the
social, behavioral, and biological sciences. All of the analyses
included are done in R with code routinely provided.
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