Statistical Learning From A Regression Perspective (springer Texts In Statistics)
by Richard A. Berk /
2016 / English / EPUB
3.5 MB Download
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 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.
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.
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.
