Fundamentals of Predictive Analytics with JMP, Second Edition
- 5h 22m
- B. D. McCullough, Ron Klimberg
- SAS Institute
- 2016
Written for students in undergraduate and graduate statistics courses, as well as for the practitioner who wants to make better decisions from data and models, this updated and expanded second edition of 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 and predictive analytics. Going beyond the theoretical foundation, this book gives you the technical knowledge and problem-solving skills that you need to perform real-world multivariate data analysis.
First, this book teaches you to recognize when it is appropriate to use a tool, what variables and data are required, and what the results might be. Second, it teaches you how to interpret the results and then, step-by-step, how and where to perform and evaluate the analysis in JMP®.
Using JMP® 13 and JMP® 13 Pro, this book offers the following new and enhanced features in an example-driven format:
- an add-in for Microsoft Excel
- Graph Builder
- dirty data
- visualization
- regression
- ANOVA
- logistic regression
- principal component analysis
- LASSO
- elastic net
- cluster analysis
- decision trees
- k-nearest neighbors
- neural networks
- bootstrap forests
- boosted trees
- text mining
- association rules
- model comparison
With today's emphasis on business intelligence, business analytics, and predictive analytics, this second edition is invaluable to anyone who needs to expand his or her knowledge of statistics and to apply real-world, problem-solving analysis.
This book is part of the SAS Press program.
About the Authors
Ron Klimberg, PhD, is a professor at the Haub School of Business at Saint Joseph's University in Philadelphia, PA. Before joining the faculty in 1997, he was a professor at Boston University, an operations research analyst at the U.S. Food and Drug Administration, and an independent consultant. His current primary interests include multiple criteria decision making, data envelopment analysis, data visualization, data mining, and modeling in general. Klimberg was the 2007 recipient of the Tengelmann Award for excellence in scholarship, teaching, and research. He received his PhD from Johns Hopkins University and his MS from George Washington University.
B. D. McCullough, PhD, is a professor at the LeBow College of Business at Drexel University in Philadelphia, PA. Before joining Drexel, he was a senior economist at the Federal Communications Commission and an assistant professor at Fordham University. His research interests include applied econometrics and time series analysis, statistical and econometrics software accuracy, research replicability, and data mining. He received his PhD from The University of Texas at Austin.
In this Book
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Introduction
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Statistics Review
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Dirty Data
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Data Discovery with Multivariate Data
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Regression and ANOVA
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Logistic Regression
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Principal Components Analysis
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Least Absolute Shrinkage and Selection Operator and Elastic Net
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Cluster Analysis
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Decision Trees
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k-Nearest Neighbors
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Neural Networks
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Bootstrap Forests and Boosted Trees
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Model Comparison
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Text Mining
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Market Basket Analysis
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Statistical Storytelling
