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JMP 11 Specialized Models

Book Description

JMP 11 Specialized Models provides details about modeling techniques such as partitioning, neural networks, nonlinear regression, and time series analysis. Topics include the Gaussian platform, which is useful in analyzing computer simulation experiments. The book also covers the Response Screening platform, which is useful in testing the effect of a predictor when you have many responses.

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 Specialized Modeling
    1. Overview of Modeling Techniques
  4. Partition Models
    1. Use Decision Trees to Explore and Model Your Data
    2. Overview of Partition
    3. Example of Partition
    4. Launching the Partition Platform
    5. Partition Method
      1. Decision Tree
        1. Decision Tree Report for Continuous Responses
        2. Decision Tree Report for Categorical Responses
        3. Node Options
        4. Platform Options
        5. Automatic Splitting
      2. Bootstrap Forest
        1. Bootstrap Forest Fitting Options
        2. Bootstrap Forest Report
        3. Bootstrap Forest Platform Options
      3. Boosted Tree
        1. Boosted Tree Fitting Options
        2. Boosted Tree Report
        3. Boosted Tree Platform Options
    6. Validation
    7. Graphs for Goodness of Fit
      1. Actual by Predicted Plot
      2. ROC Curve
      3. Lift Curves
    8. Informative Missing
    9. Examples of Bootstrap Forest, Boosted Tree, and Model Comparison
      1. Decision Tree
      2. Bootstrap Forest
      3. Boosted Tree
      4. Compare Methods
      5. Model Comparison
    10. Statistical Details
      1. General
      2. Splitting Criterion
      3. Predicted Probabilities in Decision Tree and Bootstrap Forest
  5. Neural Networks
    1. Fit Nonlinear Models Using Nodes and Layers
    2. Overview of Neural Networks
    3. Launch the Neural Platform
      1. The Neural Launch Window
      2. The Model Launch
        1. Validation Method
        2. Hidden Layer Structure
        3. Boosting
        4. Fitting Options
    4. Model Reports
      1. Training and Validation Measures of Fit
      2. Confusion Statistics
    5. Model Options
    6. Example of a Neural Network
  6. Model Comparison
    1. Compare the Predictive Ability of Fitted Models
    2. Example of Model Comparison
    3. Launch the Model Comparison Platform
    4. The Model Comparison Report
    5. Model Comparison Platform Options
      1. Continuous and Categorical Responses
      2. Continuous Responses
      3. Categorical Responses
    6. Additional Example of Model Comparison
  7. Nonlinear Regression with Built-In Models
    1. Analyze Models with the Fit Curve Platform
    2. Introduction to the Nonlinear Fit Curve Personality
    3. Example Using the Fit Curve Personality
    4. Launch the Nonlinear Platform
    5. The Fit Curve Report
      1. Initial Fit Curve Reports
        1. Model Comparison Report
        2. Model Reports
    6. Fit Curve Options
      1. Model Formulas
      2. Test Parallelism
      3. Compare Parameter Estimates
      4. Equivalence Test
  8. Nonlinear Regression with Custom Models
    1. Analyze Models That You Create
    2. Example of Fitting a Custom Model
    3. Launch the Nonlinear Platform
    4. The Nonlinear Fit Report
    5. Nonlinear Platform Options
    6. Create a Formula Using the Model Library
      1. Customize the Nonlinear Model Library
    7. Additional Examples
      1. Example of Maximum Likelihood: Logistic Regression
      2. Example of a Probit Model with Binomial Errors: Numerical Derivatives
      3. Example of a Poisson Loss Function
    8. Statistical Details
      1. Profile Likelihood Confidence Limits
      2. How Custom Loss Functions Work
      3. Notes Concerning Derivatives
      4. Notes on Effective Nonlinear Modeling
  9. Gaussian Process
    1. Fit Data Using Smoothing Models
    2. Launching the Platform
    3. The Gaussian Process Report
      1. Actual by Predicted Plot
      2. Model Report
        1. Mu, Theta, and Sigma
      3. Marginal Model Plots
    4. Platform Options
    5. Borehole Hypercube Example
  10. Time Series Analysis
    1. Fit Time Series Models and Transfer Functions
    2. Launch the Platform
      1. Select Columns into Roles
      2. The Time Series Graph
    3. Time Series Commands
      1. Graph
      2. Autocorrelation and Partial Autocorrelation
        1. Statistical Details for Autocorrelation and Partial Autocorrelation
      3. Variogram
      4. AR Coefficients
      5. Spectral Density
      6. Save Spectral Density
      7. Number of Forecast Periods
      8. Difference
      9. Show Lag Plot
    4. Modeling Reports
      1. Model Comparison Table
      2. Model Summary Table
      3. Parameter Estimates Table
      4. Forecast Plot
      5. Residuals
      6. Iteration History
      7. Model Report Options
    5. ARIMA Model
    6. Seasonal ARIMA
    7. ARIMA Model Group
    8. Transfer Functions
      1. Report and Menu Structure
      2. Diagnostics
      3. Model Building
      4. Transfer Function Model
      5. Model Reports
      6. Model Comparison Table
      7. Fitting Notes
    9. Smoothing Models
      1. Simple Moving Average
      2. Smoothing Model Dialog
      3. Simple Exponential Smoothing
      4. Double (Brown) Exponential Smoothing
      5. Linear (Holt) Exponential Smoothing
      6. Damped-Trend Linear Exponential Smoothing
      7. Seasonal Exponential Smoothing
      8. Winters Method (Additive)
  11. Response Screening
    1. Screen Large-Scale Data
    2. Response Screening Platform Overview
    3. Example of Response Screening
    4. Launch the Response Screening Platform
    5. The Response Screening Report
      1. FDR PValue Plot
      2. FDR LogWorth by Effect Size
      3. FDR LogWorth by RSquare
    6. The PValues Data Table
      1. PValues Data Table Columns
      2. Columns Added for Robust Option
      3. PValues Data Table Scripts
    7. Response Screening Platform Options
      1. Means Data Table
      2. Compare Means Data Table
    8. The Response Screening Personality in Fit Model
      1. Launch Response Screening in Fit Model
      2. The Fit Response Screening Report
      3. PValues Data Table
      4. Y Fits Data Table
    9. Additional Examples of Response Screening
      1. Example of Tests of Practical Significance and Equivalence
      2. Example of the MaxLogWorth Option
      3. Example of Robust Fit
      4. Response Screening Personality
    10. Statistical Details
      1. The False Discovery Rate
  12. References
  13. Statistical Details
    1. Specialized Models
    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
  14. Index
    1. Specialized Models
    2. Symbols
    3. Numerics
    4. A
    5. B
    6. C
    7. D
    8. E
    9. F
    10. G
    11. H
    12. I
    13. J
    14. K
    15. L
    16. M
    17. N
    18. O
    19. P
    20. Q
    21. R
    22. S
    23. T
    24. U
    25. V
    26. W-Z