Fundamentals of Predictive Analytics with JMP, Third Edition
eBook - ePub

Fundamentals of Predictive Analytics with JMP, Third Edition

  1. 494 pages
  2. English
  3. ePUB (mobile friendly)
  4. Available on iOS & Android
eBook - ePub

Fundamentals of Predictive Analytics with JMP, Third Edition

About this book

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.

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Yes, you can access Fundamentals of Predictive Analytics with JMP, Third Edition by Ron Klimberg in PDF and/or ePUB format, as well as other popular books in Computer Science & Computer Science General. We have over one million books available in our catalogue for you to explore.

Table of contents

  1. Cover
  2. Copyright Page
  3. Contents
  4. About This Book
  5. About The Author
  6. Acknowledgments
  7. Dedication
  8. Chapter 1: Introduction
  9. Chapter 2: Statistics Review
  10. Chapter 3: Dirty Data
  11. Chapter 4: Data Discovery with Multivariate Data
  12. Chapter 5: Regression and ANOVA
  13. Chapter 6: Logistic Regression
  14. Chapter 7: Principal Components Analysis
  15. Chapter 8: Least Absolute Shrinkage and Selection Operator and Elastic Net
  16. Chapter 9: Cluster Analysis
  17. Chapter 10: Decision Trees
  18. Chapter 11: k-Nearest Neighbors
  19. Chapter 12: Neural Networks
  20. Chapter 13: Bootstrap Forests and Boosted Trees
  21. Chapter 14: Model Comparison
  22. Chapter 15: Text Mining
  23. Chapter 16: Market Basket Analysis
  24. Chapter 17: Time Series Forecasting
  25. Chapter 18: Statistical Storytelling
  26. References
  27. Index