Fundamentals of Predictive Analytics with JMP

by Ron Klimberg

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.

408 pages, Kindle Edition

First published April 27, 2012