An Introduction to Statistical Learning: with Applications in R Springer Texts in Statistics | 2nd ed. 2021 Edition

Compare Textbook Prices for An Introduction to Statistical Learning: with Applications in R Springer Texts in Statistics 2nd ed. 2021 Edition ISBN 9781071614204 by James, Gareth,Witten, Daniela,Hastie, Trevor,Tibshirani, Robert
Authors: James, Gareth,Witten, Daniela,Hastie, Trevor,Tibshirani, Robert
ISBN:1071614207
ISBN-13: 9781071614204
List Price: $37.55 (up to 16% savings)
Prices shown are the lowest from
the top textbook retailers.

View all Prices by Retailer

Details about An Introduction to Statistical Learning: with Applications in R Springer Texts in Statistics:

An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, deep learning, survival analysis, multiple testing, and more. Color graphics and real-world examples are used to illustrate the methods presented. Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform. Two of the authors co-wrote The Elements of Statistical Learning (Hastie, Tibshirani and Friedman, 2nd edition 2009), a popular reference book for statistics and machine learning researchers. An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience. This book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data. The text assumes only a previous course in linear regression and no knowledge of matrix algebra. This Second Edition features new chapters on deep learning, survival analysis, and multiple testing, as well as expanded treatments of naïve Bayes, generalized linear models, Bayesian additive regression trees, and matrix completion. R code has been updated throughout to ensure compatibility.

Need a Statistics tutor? View profile below:
Deron P.
(0 reviews)
Education: Waldorf MD
Major: ASVAB Tutor

I tutor Accounting and Statistics students, specializing in Financial Accounting and Intro to Statistics. I also teach in group settings at discounted rates. For one-on-one sessions, I offer completely individualized tutoring - according to to my student's strengths and weaknesses. You'll find that I have a unique ability to assess your knowledge gaps and develop ways to close those gaps. I have more than 6 years' experience in bank regulation with the Federal Reserve and FDIC. My experienc ... Read more

I tutor Accounting and Statistics students, specializing in Financial Accounting and Intro to Statistics. I also teach in group settings at discounted rates. For one-on-one sessions, I offer completely individualized tutoring - according to to my student's strengths and weaknesses. You'll find that I have a unique ability to assess your knowledge gaps and develop ways to close those gaps. I have more than 6 years' experience in bank regulation with the Federal Reserve and FDIC. My experienc ... Read more

Need Statistics course notes? Start your search below: