Fundamentals of Predictive Analytics with JMP, Second Edition: Edition 2

Ron Klimberg · B. D. McCullough

Dec 2017 · SAS Institute

About this ebook

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(R) 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.

Ratings and reviews

This book is one of the few 5-star books in my predictive analytics library. I've read more books on predictive analytics in my data science studies than practically any other topic. And this one really sticks out for the following qualities: Clear, concise writing Easy to understand word choices Lack of statistical pretension A sequential approach to building from bottom to top of your knowledge base Fewer foundational knowledge assumptions than my other predictive analytics books And trust me when I say that after the past 7 classes in data science, this one's a gem. Do yourself a favor and start out here if you're starting out or come back to this book if you're getting confused and need to read the tried and true in common English.

Text is great. "Digital" version of text requires Adobe to read despite noting it may be downloaded as pdf or epub. I have the hardcopy and wanted the digital version for convenience, didn't work out that way.

About the author

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.