## Book description

Whether your model is deterministic, or involves necessary â€œnoiseâ€ as well as a â€œsignal,â€ JMP is equipped to handle your modeling needs. JMP 11 Multivariate Methods shows you how to take advantage of the modeling platforms Multivariate, Cluster, Discriminant, Principal Components, and Partial Least Squares.

1. Contents
2. Formatting Conventions
3. JMP Documentation
4. Additional Resources for Learning JMP
3. Introduction to Multivariate Analysis
4. Correlations and Multivariate Techniques
1. Explore the Multidimensional Behavior of Variables
2. Launch the Multivariate Platform
1. Estimation Methods
3. The Multivariate Report
4. Multivariate Platform Options
1. Nonparametric Correlations
2. Scatterplot Matrix
3. Outlier Analysis
4. Item Reliability
5. Impute Missing Data
5. Examples
6. Computations and Statistical Details
1. Estimation Methods
2. Pearson Product-Moment Correlation
3. Nonparametric Measures of Association
4. Inverse Correlation Matrix
5. Distance Measures
6. Cronbach’s α
5. Cluster Analysis
1. Identify and Explore Groups of Similar Objects
2. Introduction to Clustering Methods
3. The Cluster Launch Dialog
4. Hierarchical Clustering
5. K-Means Clustering
1. K-Means Control Panel
2. K-Means Report
6. Normal Mixtures
7. Self Organizing Maps
6. Principal Components
7. Discriminant Analysis
1. Predict Classifications Based on Continuous Variables
2. Introduction
3. Discriminating Groups
4. Commands and Options
5. Validation
8. Partial Least Squares Models
1. Develop Models Using Correlations Between Ys and Xs
2. Overview of the Partial Least Squares Platform
3. Example of Partial Least Squares
4. Launch the Partial Least Squares Platform
5. Model Launch Control Panel
6. The Partial Least Squares Report
7. Partial Least Squares Options
8. Model Fit Options
9. Statistical Details
9. References
10. Statistical Details
1. Multivariate Methods
2. The Response Models
1. Continuous Responses
2. Nominal Responses
3. Ordinal Responses
3. The Factor Models
1. Continuous Factors
2. Nominal Factors
3. Ordinal Factors
4. The Usual Assumptions
5. Key Statistical Concepts
1. Uncertainty, a Unifying Concept
2. The Two Basic Fitting Machines
6. Multivariate Details
7. Power Calculations
8. Inverse Prediction with Confidence Limits
11. Index

## Product information

• Title: JMP 11 Multivariate Methods
• Author(s): SAS Institute
• Release date: September 2013
• Publisher(s): SAS Institute
• ISBN: 9781612906751