Statistical Learning from a Regression Perspective (Paperback, Softcover reprint of the original 2nd ed. 2016)

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.

General

Imprint:	Springer International Publishing AG
Country of origin:	Switzerland
Series:	Springer Texts in Statistics
Release date:	June 2018
First published:	2016
Authors:	Richard A. Berk
Dimensions:	235 x 155 x 20mm (L x W x T)
Format:	Paperback
Pages:	347
Edition:	Softcover reprint of the original 2nd ed. 2016
ISBN-13:	978-3-319-82969-2
Categories:	Books > Science & Mathematics > Mathematics > Probability & statistics Books > Social sciences > Sociology, social studies > Social research & statistics > General Books > Social sciences > Psychology > Psychological methodology > General Books > Medicine > General issues > Public health & preventive medicine > General Promotions
LSN:	3-319-82969-6
Barcode:	9783319829692

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