
- 494 pages
- English
- ePUB (mobile friendly)
- Available on iOS & Android
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.
Frequently asked questions
- Essential is ideal for learners and professionals who enjoy exploring a wide range of subjects. Access the Essential Library with 800,000+ trusted titles and best-sellers across business, personal growth, and the humanities. Includes unlimited reading time and Standard Read Aloud voice.
- Complete: Perfect for advanced learners and researchers needing full, unrestricted access. Unlock 1.4M+ books across hundreds of subjects, including academic and specialized titles. The Complete Plan also includes advanced features like Premium Read Aloud and Research Assistant.
Please note we cannot support devices running on iOS 13 and Android 7 or earlier. Learn more about using the app.
Information
Table of contents
- Cover
- Copyright Page
- Contents
- About This Book
- About The Author
- Acknowledgments
- Dedication
- Chapter 1: Introduction
- Chapter 2: Statistics Review
- Chapter 3: Dirty Data
- Chapter 4: Data Discovery with Multivariate Data
- Chapter 5: Regression and ANOVA
- Chapter 6: Logistic Regression
- Chapter 7: Principal Components Analysis
- Chapter 8: Least Absolute Shrinkage and Selection Operator and Elastic Net
- Chapter 9: Cluster Analysis
- Chapter 10: Decision Trees
- Chapter 11: k-Nearest Neighbors
- Chapter 12: Neural Networks
- Chapter 13: Bootstrap Forests and Boosted Trees
- Chapter 14: Model Comparison
- Chapter 15: Text Mining
- Chapter 16: Market Basket Analysis
- Chapter 17: Time Series Forecasting
- Chapter 18: Statistical Storytelling
- References
- Index