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Using Classification and Regression Trees (A Practical Primer)

List Price: $54.00
SKU:
9781641132374
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  • Product Details

    Author:
    Xin Ma
    Format:
    Paperback
    Pages:
    166
    Publisher:
    Emerald Publishing Limited (March 27, 2018)
    Imprint:
    Information Age Publishing
    Language:
    English
    Audience:
    Professional and scholarly
    ISBN-13:
    9781641132374
    ISBN-10:
    164113237X
    Weight:
    8.48oz
    Dimensions:
    6.14" x 9.21" x 0.35"
    File:
    TWO RIVERS-PERSEUS-Metadata_Only_Perseus_Distribution_Customer_Group_Metadata_20251017163424-20251018.xml
    Folder:
    TWO RIVERS
    List Price:
    $54.00
    Country of Origin:
    United Kingdom
    Pub Discount:
    35
    Case Pack:
    1
    As low as:
    $51.30
    Publisher Identifier:
    P-PER
    Discount Code:
    H
  • Overview

    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.