Using Classification and Regression Trees | A Practical Primer

Xin Ma

ISBN 10: 164113237X / ISBN 13: 9781641132374

Published by Information Age Publishing, 2018

About this Item

Using Classification and Regression Trees | A Practical Primer | Xin Ma | Taschenbuch | Einband - flex.(Paperback) | Englisch | 2018 | Information Age Publishing | EAN 9781641132374 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.

Seller Inventory # 116782502

Bibliographic Details

• Title: Using Classification and Regression Trees | A Practical Primer
• Publisher: Information Age Publishing
• Publication Date: 2018
• Language: English
• ISBN 10: 164113237X
• ISBN 13: 9781641132374
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 234W x 156H x 10D millimeters 234 millimeters width by 156 millimeters height by 10 millimeters depth
• Weight: 263 grams
• Seller catalogs:
  • Bücher

About this title

Synopsis

Classification and regression trees (CART) is one of the several contemporary statistical techniques with good promise for research in many academic fields. There are very few books on CART, especially on applied CART.

This book, as a good practical primer with a focus on applications, introduces the relatively new statistical technique of CART as a powerful analytical tool. The easy-to-understand (non-technical) language and illustrative graphs (tables) as well as the use of the popular statistical software program (SPSS) appeal to readers without strong statistical background. This book helps readers understand the foundation, the operation, and the interpretation of CART analysis, thus becoming knowledgeable consumers and skillful users of CART.

The chapter on advanced CART procedures not yet well-discussed in the literature allows readers to effectively seek further empowerment of their research designs by extending the analytical power of CART to a whole new level. This highly practical book is specifically written for academic researchers, data analysts, and graduate students in many disciplines such as economics, social sciences, medical sciences, and sport sciences who do not have strong statistical background but still strive to take full advantage of CART as a powerful analytical tool for research in their fields.