Fundamentals of Predictive Analytics with JMP, Third Edition

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.

1143402674
Fundamentals of Predictive Analytics with JMP, Third Edition

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.

74.99 In Stock
Fundamentals of Predictive Analytics with JMP, Third Edition

Fundamentals of Predictive Analytics with JMP, Third Edition

by Ron Klimberg
Fundamentals of Predictive Analytics with JMP, Third Edition
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Fundamentals of Predictive Analytics with JMP, Third Edition

Fundamentals of Predictive Analytics with JMP, Third Edition

by Ron Klimberg

Overview

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.

Product Details

ISBN-13: 9781685800017
Publisher: SAS Institute
Publication date: 04/18/2023
Sold by: Barnes & Noble
Format: eBook
Pages: 494
File size: 21 MB
Note: This product may take a few minutes to download.

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.
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