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JMP 12 Multivariate Methods

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.

Table of Contents

  1. Contents
  2. Learn about JMP
    1. Documentation and Additional Resources
    2. Formatting Conventions
    3. JMP Documentation
      1. JMP Documentation Library
      2. Discovering JMP
      3. Using JMP
      4. Basic Analysis
      5. Essential Graphing
      6. Profilers
      7. Design of Experiments Guide
      8. Fitting Linear Models
      9. Specialized Models
      10. Multivariate Methods
      11. Quality and Process Methods
      12. Reliability and Survival Methods
      13. Consumer Research
      14. Scripting Guide
      15. JSL Syntax Reference
      16. JMP Help
    4. Additional Resources for Learning JMP
      1. Tutorials
      2. Sample Data Tables
      3. Learn about Statistical and JSL Terms
      4. Learn JMP Tips and Tricks
      5. Tooltips
      6. JMP User Community
      7. JMPer Cable
      8. JMP Books by Users
      9. The JMP Starter Window
  3. Introduction to Multivariate Analysis
    1. Overview of Multivariate Techniques
  4. Correlations and Multivariate Techniques
    1. Explore the Multidimensional Behavior of Variables
    2. Launch the Multivariate Platform
      1. Estimation Methods
        1. Default
        2. REML
        3. ML
        4. Robust
        5. Row-wise
        6. Pairwise
    3. The Multivariate Report
    4. Multivariate Platform Options
      1. Nonparametric Correlations
      2. Scatterplot Matrix
      3. Outlier Analysis
        1. Mahalanobis Distance
        2. Jackknife Distances
        3. T2 Statistic
        4. Saving Distances and Values
      4. Item Reliability
      5. Impute Missing Data
    5. Example of Item Reliability
    6. Computations and Statistical Details
      1. Estimation Methods
        1. Robust
      2. Pearson Product-Moment Correlation
      3. Nonparametric Measures of Association
        1. Spearman’s ρ (rho) Coefficients
        2. Kendall’s τb Coefficients
        3. Hoeffding’s D Statistic
      4. Inverse Correlation Matrix
      5. Distance Measures
        1. Mahalanobis Distance Measures
        2. Jackknife Distance Measures
        3. T2 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
      1. Hierarchical Cluster Report
      2. Hierarchical Cluster Options
    6. K-Means Clustering
      1. K-Means Control Panel
      2. K-Means Report
        1. K-Means Platform Options
    7. Normal Mixtures
      1. Robust Normal Mixtures
      2. Platform Options
    8. Self Organizing Maps
      1. Implementation Technical Details
    9. Additional Examples of Cluster Analysis
      1. Example of Self-Organizing Maps
    10. Statistical Details
      1. Statistical Details for Hierarchical Clustering
      2. Statistical Details for Robust Estimation Methods
  6. 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
    5. Principal Components on Correlations Report
    6. Principal Components Platform Options
  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
        1. Updating the F Ratio and Prob>F
        2. Statistics
        3. Buttons
        4. Columns
        5. Stepwise Example
      2. Discriminant Methods
        1. Regularized, Compromise Method
      3. Shrink Covariances
    5. The Discriminant Analysis Report
      1. Canonical Plot
        1. Classification into Three or More Categories
        2. Classification into Two Categories
      2. Discriminant Scores
      3. Score Summaries
        1. Entropy RSquare
    6. Discriminant Analysis Options
      1. Score Options
      2. Canonical Options
        1. Show Canonical Details
        2. Show Canonical Structure
      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. Between Groups Covariance Matrix
  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
      1. Centering and Scaling
      2. Standardize X
    5. Model Launch Control Panel
    6. Partial Least Squares Report
      1. Model Comparison Summary
      2. <Validation Method> Cross Validation
        1. Root Mean PRESS Plot
        2. Root Mean PRESS
        3. Calculation of Q2
        4. Calculation of R2X and R2Y When Validation Is Used
      3. Model Fit Report
    7. Partial Least Squares Options
    8. Model Fit Options
      1. Variable Importance Plot
      2. VIP vs Coefficients Plots
      3. Save Columns
    9. Statistical Details
      1. Partial Least Squares
        1. NIPALS
        2. SIMPLS
      2. van der Voet T2
      3. T2 Plot
      4. Confidence Ellipses for X Score Scatterplot Matrix
      5. Standard Error of Prediction and Confidence Limits
        1. Standard Error of Prediction Formula
        2. Mean Confidence Limit Formula
        3. Indiv Confidence Limit Formula
      6. Standardized Scores and Loadings
        1. Standardized Scores
        2. Standardized Loadings
  9. References
  10. Statistical Details
    1. Multivariate Methods
    2. Multivariate Tests
    3. Approximate F-Test
    4. Canonical Details
    5. Discriminant Analysis
  11. Index
    1. Multivariate Methods
    2. Numerics
    3. A
    4. B
    5. C
    6. D
    7. E
    8. F
    9. G
    10. H
    11. I
    12. J
    13. K
    14. L
    15. M
    16. N
    17. O
    18. P
    19. Q
    20. R
    21. S
    22. T
    23. U
    24. W-Z