Fundamentals of predictive analytics with JMP

"Fundamentals of Predictive Analytics with JMP" bridges the gap between courses on basic statistics, which focus on univariate and bivariate analysis, and courses on data mining/predictive analytics. It provides the technical knowledge and problem-solving skills needed to perform real data...

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Bibliographic Details
Main Author: Klimberg, Ronald K.
Other Authors: McCullough, B. D.
Format: eBook
Language:English
Published: Cary, NC SAS Institute 2013
Subjects:
Online Access:
Collection: O'Reilly - Collection details see MPG.ReNa
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300 |a xvi, 274 pages  |b illustrations 
505 0 |a Introduction -- Statistics review -- Introduction to multivariate data -- Regression and ANOVA review -- Logistic regression -- Principal components analysis -- Cluster analysis -- Decision trees -- Neural networks -- Model comparison -- Telling the statistical story 
505 0 |a Includes bibliographical references and index 
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520 |a "Fundamentals of Predictive Analytics with JMP" bridges the gap between courses on basic statistics, which focus on univariate and bivariate analysis, and courses on data mining/predictive analytics. It provides the technical knowledge and problem-solving skills needed to perform real data multivariate analysis. Utilizing JMP 10 and JMP Pro, the book offers new and enhanced resources, including an add-in to Microsoft Excel, Graph Builder, and data mining capabilities. Written for students in undergraduate and graduate-level statistics courses, this book first teaches students to recognize when it is appropriate to use the tool, to understand what variables and data are required, and to know what the results might be. Second, it teaches them how to interpret the results, followed by step-by-step instructions on how and where to perform and evaluate the analysis in JMP. With the new emphasis on business intelligence, business analytics and predictive analytics, this book is invaluable to everyone who needs to expand their knowledge of statistics and apply real problem-solving analysis