## Book Description

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(R) 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.

3. Acknowledgments
4. Chapter 1: Introduction
5. Historical Perspective
6. Two Questions Organizations Need to Ask
8. Introductory Statistics Courses
9. Practical Statistical Study
10. Framework and Chapter Sequence
11. Chapter 2: Statistics Review
12. Introduction
13. Fundamental Concepts 1 and 2
14. Fundamental Concept 3: Understand a Z-Score
15. Fundamental Concept 4
16. Fundamental Concept 5
17. Fundamental Concept 6:
18. Chapter 3: Dirty Data
19. Introduction
20. Data Set
21. Error Detection
22. Outlier Detection
23. General First Steps on Receipt of a Data Set
24. Exercises
25. Chapter 4: Data Discovery with Multivariate Data
26. Introduction
27. Use Tables to Explore Multivariate Data
28. Use Graphs to Explore Multivariate Data
29. Explore a Larger Data Set
30. Explore a Real-World Data Set
31. Chapter 5: Regression and ANOVA
32. Introduction
33. Regression
34. Analysis of Variance
35. Exercises
36. Chapter 6: Logistic Regression
37. Introduction
38. A Straightforward Example Using JMP
39. A Realistic Logistic Regression Statistical Study
40. Exercises
41. Chapter 7: Principal Components Analysis
42. Introduction
43. Basic Steps in JMP
44. Dimension Reduction
45. Discovery of Structure in the Data
46. Exercises
47. Chapter 8: Least Absolute Shrinkage and Selection Operator and Elastic Net
48. Introduction
49. Least Absolute Shrinkage and Selection Operator
50. Elastic Net
51. Exercises
52. Chapter 9: Cluster Analysis
53. Introduction
54. Hierarchical Clustering
55. K-Means Clustering
56. K-Means Clustering versus Hierarchical Clustering
57. Exercises
58. Chapter 10: Decision Trees
59. Introduction
60. Classification Trees
61. Regression Trees
62. Exercises
63. Chapter 11: k-Nearest Neighbors
64. Introduction
65. k-Nearest Neighbors Analysis
66. k-Nearest Neighbor for Multiclass Problems
67. The k-Nearest Neighbor Regression Models
68. Limitations and Drawbacks of the Technique
69. Exercises
70. Chapter 12: Neural Networks
71. Introduction
72. Understand Validation Methods
73. Understand the Hidden Layer Structure
74. Understand Options for Improving the Fit of a Model
75. Complete the Data Preparation
76. Use JMP on an Example Data Set
77. Exercises
78. Chapter 13: Bootstrap Forests and Boosted Trees
79. Introduction
80. Bootstrap Forests
81. Boosted Trees
82. Exercises
83. Chapter 14: Model Comparison
84. Introduction
85. Perform a Model Comparison with Continuous Dependent Variable
86. Perform a Model Comparison with Binary Dependent Variable
87. Perform a Model Comparison Using the Lift Chart
88. Train, Validate, and Test
89. Exercises
90. Chapter 15: Text Mining
91. Introduction
92. Developing the Document Term Matrix
93. Developing the Document Term Matrix with a Larger Data Set
94. Using Multivariate Techniques
95. Using Predictive Techniques
96. Exercises
97. Chapter 16: Market Basket Analysis
98. Introduction
99. Understand Support, Confidence, and Lift
100. Use JMP to Calculate Confidence and Lift
101. Analyze a Real Data Set
102. Exercises
103. Chapter 17: Statistical Storytelling
104. The Path from Multivariate Data to the Modeling Process
105. Definitions of Data Mining
106. A Framework for Predictive Analytics Techniques
107. The Goal, Tasks, and Phases of Predictive Analytics
108. References
109. Index

## Product Information

• Title: Fundamentals of Predictive Analytics with JMP, Second Edition
• Author(s): Ron Klimberg, B. D. McCullough
• Release date: December 2017
• Publisher(s): SAS Institute
• ISBN: 9781629608013