JMP 11 Multivariate Methods

Book description

Whether your model is deterministic, or involves necessary “noise” as well as a “signal,” 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.

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. 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. Examples
      1. 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
      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
      1. Hierarchical Cluster Options
      2. Technical Details for Hierarchical Clustering
    5. K-Means Clustering
      1. K-Means Control Panel
      2. K-Means Report
        1. K-Means Platform Options
    6. Normal Mixtures
      1. Robust Normal Mixtures
      2. Platform Options
      3. Details of the Estimation Process
    7. Self Organizing Maps
      1. Implementation Technical Details
  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. Introduction
    3. Discriminating Groups
      1. Discriminant Method
      2. Stepwise Selection
      3. Canonical Plot
      4. Score Summaries
      5. Discriminant Scores
    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
      1. Centering and Scaling
      2. Standardize X
    5. Model Launch Control Panel
    6. The Partial Least Squares Report
      1. Model Comparison Summary
      2. Cross Validation
      3. Model Fit Report
    7. Partial Least Squares Options
    8. Model Fit Options
      1. Variable Importance Plot
      2. VIP vs Coefficients Plots
    9. Statistical Details
      1. Partial Least Squares
      2. NIPALS
      3. SIMPLS
      4. T2 Plot
      5. van der Voet T2
      6. Confidence Ellipse for Scatter Scores Plots
  9. References
  10. Statistical Details
    1. Multivariate Methods
    2. The Response Models
      1. Continuous Responses
        1. Fitting Principle for Continuous Response
        2. Base Model
      2. Nominal Responses
        1. Fitting Principle For Nominal Response
        2. Base Model
      3. Ordinal Responses
        1. Fitting Principle For Ordinal Response
        2. Base Model
    3. The Factor Models
      1. Continuous Factors
      2. Nominal Factors
        1. Interpretation of Parameters
        2. Interactions and Crossed Effects
        3. Nested Effects
        4. Least Squares Means across Nominal Factors
        5. Effective Hypothesis Tests
        6. Singularities and Missing Cells in Nominal Effects
      3. Ordinal Factors
        1. Ordinal Interactions
        2. Hypothesis Tests for Ordinal Crossed Models
        3. Ordinal Least Squares Means
        4. Singularities and Missing Cells in Ordinal Effects
        5. Example with Missing Cell
    4. The Usual Assumptions
      1. Assumed Model
      2. Relative Significance
      3. Multiple Inferences
      4. Validity Assessment
      5. Alternative Methods
    5. Key Statistical Concepts
      1. Uncertainty, a Unifying Concept
      2. The Two Basic Fitting Machines
        1. Springs
        2. Pressure Cylinders
    6. Multivariate Details
      1. Multivariate Tests
      2. Approximate F-Test
      3. Canonical Details
      4. Discriminant Analysis
    7. Power Calculations
      1. Computations for the LSN
      2. Computations for the LSV
      3. Computations for the Power
      4. Computations for the Adjusted Power
    8. Inverse Prediction with Confidence Limits
  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. V
    25. W-Z

Product information

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