Fundamentals of Predictive Analytics with JMP, Third Edition
- 6h 33m
- Ron Klimberg
- SAS Institute
- 2023
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 third 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.
Using JMP® 17, this book discusses 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
- time series forecasting
With a new, expansive chapter on time series forecasting and more exercises to test your skills, this third edition is invaluable to those who need to expand their knowledge of statistics and apply real-world, problem-solving analysis.
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.
In this Book
-
About This Book
-
Introduction
-
Statistics Review
-
Dirty Data
-
Data Discovery with Multivariate Data
-
Regression and ANOVA
-
Logistic Regression
-
Principal Components Analysis
-
Least Absolute Shrinkage and Selection Operator and Elastic Net
-
Cluster Analysis
-
Decision Trees
-
k-Nearest Neighbors
-
Neural Networks
-
Bootstrap Forests and Boosted Trees
-
Model Comparison
-
Text Mining
-
Market Basket Analysis
-
Time Series Forecasting
-
Statistical Storytelling
-
References
