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## Book Description

JMP 12 Multivariate Methods describes techniques for analyzing several variables simultaneously. The book covers descriptive measures, such as correlations. It also describes methods that give insight into the structure of the multivariate data, such as clustering, principal components, discriminant analysis, 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. Example of Item Reliability
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. Clustering Overview
3. Example of Clustering
4. Launch the Cluster Platform
5. Hierarchical Clustering
6. K-Means Clustering
1. K-Means Control Panel
2. K-Means Report
7. Normal Mixtures
8. Self Organizing Maps
9. Additional Examples of Cluster Analysis
10. Statistical Details
6. Principal Components
7. Discriminant Analysis
1. Predict Classifications Based on Continuous Variables
2. Discriminant Analysis Overview
3. Example of Discriminant Analysis
4. Discriminant Launch Window
1. Stepwise Variable Selection
2. Discriminant Methods
3. Shrink Covariances
5. The Discriminant Analysis Report
1. Canonical Plot
2. Discriminant Scores
3. Score Summaries
6. Discriminant Analysis Options
1. Score Options
2. Canonical Options
3. Example of a Canonical 3D Plot
4. Specify Priors
5. Consider New Levels
6. Save Discrim Matrices
7. Scatterplot Matrix
7. Validation in JMP and JMP Pro
8. Technical Details
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. Partial Least Squares Report
1. Model Comparison Summary
2. <Validation Method> Cross Validation
3. Model Fit Report
7. Partial Least Squares Options
8. Model Fit Options
9. Statistical Details
1. Partial Least Squares
2. van der Voet T2
3. T2 Plot
4. Confidence Ellipses for X Score Scatterplot Matrix
5. Standard Error of Prediction and Confidence Limits