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Autor(en): 
  • Xin Ma
  • Using Classification and Regression Trees: A Practical Primer 
     

    (Buch)
    Dieser Artikel gilt, aufgrund seiner Grösse, beim Versand als 2 Artikel!


    Übersicht

    Auf mobile öffnen
     
    Lieferstatus:   i.d.R. innert 7-14 Tagen versandfertig
    Veröffentlichung:  März 2018  
    Genre:  Psychologie / Pädagogik 
     
    Pädagogik / Erziehungswissenschaften
    ISBN:  9781641132374 
    EAN-Code: 
    9781641132374 
    Verlag:  Information Age Publishing 
    Einband:  Kartoniert  
    Sprache:  English  
    Dimensionen:  H 234 mm / B 156 mm / D 10 mm 
    Gewicht:  263 gr 
    Seiten:  166 
    Bewertung: Titel bewerten / Meinung schreiben
    Inhalt:
    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.

      



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