Title
An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics),Used
Delivery time: 8-12 business days (International)
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, treebased methods, support vector machines, clustering, and more. Color graphics and realworld 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 cowrote 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 nonstatisticians alike who wish to use cuttingedge statistical learning techniques to analyze their data. The text assumes only a previous course in linear regression and no knowledge of matrix algebra.
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Shipping & Returns
Shipping
We ship your order within 2–3 business days for USA deliveries and 5–8 business days for international shipments. Once your package has been dispatched from our warehouse, you'll receive an email confirmation with a tracking number, allowing you to track the status of your delivery.
Returns
To facilitate a smooth return process, a Return Authorization (RA) Number is required for all returns. Returns without a valid RA number will be declined and may incur additional fees. You can request an RA number within 15 days of the original delivery date. For more details, please refer to our Return & Refund Policy page.
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Warranty
We provide a 2-year limited warranty, from the date of purchase for all our products.
If you believe you have received a defective product, or are experiencing any problems with your product, please contact us.
This warranty strictly does not cover damages that arose from negligence, misuse, wear and tear, or not in accordance with product instructions (dropping the product, etc.).
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Frequently Asked Questions
- Q: What topics are covered in An Introduction to Statistical Learning? A: The book covers key topics such as linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, and clustering, among others.
- Q: Who is the target audience for this book? A: This book is aimed at both statisticians and non-statisticians who wish to apply statistical learning techniques to their data analysis.
- Q: What is the primary purpose of An Introduction to Statistical Learning? A: The primary purpose is to provide an accessible overview of statistical learning techniques and facilitate their use by practitioners across various fields.
- Q: Does this book include practical examples or exercises? A: Yes, each chapter includes tutorials that demonstrate how to implement the methods using R, along with real-world examples to illustrate the concepts.
- Q: What is the binding type of this book? A: An Introduction to Statistical Learning is available in hardcover binding.
- Q: How many pages does An Introduction to Statistical Learning have? A: The book contains a total of 426 pages.
- Q: When was An Introduction to Statistical Learning published? A: The book was published on January 1, 2013.
- Q: Is prior knowledge of statistics required to understand this book? A: The book assumes that readers have completed a course in linear regression but does not require knowledge of matrix algebra.
- Q: Can this book be useful for fields outside of statistics? A: Yes, it is designed to be useful for professionals in various fields, including biology, finance, marketing, and astrophysics.
- Q: Who are the authors of this book? A: The authors of An Introduction to Statistical Learning are Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani.