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Book Description

JMP 13 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, latent class analysis, principal components, discriminant analysis, and partial least squares.

1. Contents
2. Formatting Conventions
3. JMP Documentation
4. Additional Resources for Learning JMP
5. Technical Support
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. Principal Components
1. Reduce the Dimensionality of Your Data
2. Overview of Principal Component Analysis
3. Example of Principal Component Analysis
4. Launch the Principal Components Platform
1. Estimation Methods
5. Principal Components Report
6. Principal Components Report Options
6. 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. Principal Components
2. Canonical Plot and Canonical Structure
3. Discriminant Scores
4. 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
1. Description of the Wide Linear Algorithm
2. Saved Formulas
3. Multivariate Tests
4. Approximate F-Tests
5. Between Groups Covariance Matrix
7. 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. <Cross Validation Method> and Method = <Method Specification>
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
7. PLS Discriminant Analysis (PLS-DA)
8. Hierarchical Cluster
1. Group Observations Using a Tree of Clusters
2. Hierarchical Cluster Overview
3. Example of Clustering
4. Launch the Hierarchical Cluster Platform
1. Clustering Method
2. Method for Distance Calculation
3. Data Structure
4. Transformations to Y, Columns Variables
5. Hierarchical Cluster Report
1. Dendrogram Report
2. Illustration of Dendrogram and Distance Graph
3. Clustering History Report
4. Hierarchical Cluster Options
6. Additional Examples of the Hierarchical Clustering Platform
7. Statistical Details
1. Spatial Measures
2. Distance Method Formulas
9. K Means Cluster
1. Group Observations Using Distances
2. K Means Cluster Platform Overview
3. Example of K Means Cluster
4. Launch the K Means Cluster Platform
5. Iterative Clustering Report
6. Iterative Clustering Control Panel
7. K Means NCluster=<k> Report
8. Self Organizing Map
10. Normal Mixtures
1. Group Observations Using Probabilities
2. Normal Mixtures Clustering Platform Overview
3. Example of Normal Mixtures Clustering
4. Launch the Normal Mixtures Clustering Platform
5. Iterative Clustering Report
6. Iterative Clustering Control Panel
7. Normal Mixtures NCluster=<k> Report
8. Robust Normal Mixtures
9. Statistical Details for the Normal Mixtures Method
11. Latent Class Analysis
12. Cluster Variables
1. Group Similar Variables into Representative Groups
2. Cluster Variables Platform Overview
3. Example of the Cluster Variables Platform
4. Launch the Cluster Variables Platform
5. The Cluster Variables Report
6. Cluster Variables Platform Options
7. Additional Examples of the Cluster Variables Platform
1. Example of Color Map on Correlations
2. Example of Cluster Variables Platform for Dimension Reduction
8. Statistical Details for the Cluster Variables Platform
13. Statistical Details
1. Multivariate Methods
2. Wide Linear Methods and the Singular Value Decomposition
14. References
15. Index