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by SAS Institute
JMP 10 Modeling and Multivariate Methods
Cover Page
Title Page
Copyright Page
Contents
Learn About JMP
Book Conventions
JMP Documentation
JMP Documentation Suite
JMP Help
JMP Books by Users
JMPer Cable
Additional Resources for Learning JMP
Tutorials
The JMP Starter Window
Sample Data Tables
Learn about Statistical and JSL Terms
Learn JMP Tips and Tricks
Tooltips
Access Resources on the Web
Launch the Fit Model Platform
Example of the Fit Model Platform
Specify Different Model Types
Construct Model Effects
Add
Cross
Nest
Macros
Attributes
Transformations
Fitting Personalities
Emphasis Options for Standard Least Squares
Model Specification Options
Validity Checks
Fitting Standard Least Squares Models
Example Using Standard Least Squares
The Standard Least Squares Report and Options
Regression Reports
Estimates
Effect Screening
Factor Profiling
Row Diagnostics
Save Columns
Effect Options
Restricted Maximum Likelihood (REML) Method
Method of Moments Results
Singularity Details
Examples with Statistical Details
Fitting Stepwise Regression Models
Overview of Stepwise Regression
Example Using Stepwise Regression
The Stepwise Report
Stepwise Platform Options
Stepwise Regression Control Panel
Current Estimates Report
Step History Report
Models with Crossed, Interaction, or Polynomial Terms
Models with Nominal and Ordinal Terms
Using the Make Model Command for Hierarchical Terms
Performing Logistic Stepwise Regression
The All Possible Models Option
The Model Averaging Option
Using Validation
Fitting Multiple Response Models
Example of a Multiple Response Model
The Manova Report
The Manova Fit Options
Response Specification
Choose Response Options
Custom Test Option
Multivariate Tests
The Extended Multivariate Report
Comparison of Multivariate Tests
Univariate Tests and the Test for Sphericity
Multivariate Model with Repeated Measures
Example of a Compound Multivariate Model
Discriminant Analysis
Fitting Generalized Linear Models
Overview of Generalized Linear Models
The Generalized Linear Model Personality
Examples of Generalized Linear Models
Model Selection and Deviance
Examples
Poisson Regression
Poisson Regression with Offset
Normal Regression, Log Link
Platform Commands
Performing Logistic Regression on Nominal and Ordinal Responses
Introduction to Logistic Models
The Logistic Fit Report
Logistic Plot
Iteration History
Whole Model Test
Lack of Fit Test (Goodness of Fit)
Parameter Estimates
Likelihood Ratio Tests
Logistic Fit Platform Options
Plot Options
Likelihood Ratio Tests
Wald Tests for Effects
Confidence Intervals
Odds Ratios (Nominal Responses Only)
Inverse Prediction
Save Commands
ROC Curve
Lift Curve
Confusion Matrix
Profiler
Validation
Example of a Nominal Logistic Model
Example of an Ordinal Logistic Model
Example of a Quadratic Ordinal Logistic Model
Stacking Counts in Multiple Columns
Analyzing Screening Designs
Overview of the Screening Platform
When to Use the Screening Platform
Comparing Screening and Fit Model
Launch the Screening Platform
The Screening Report
Contrasts
Half Normal Plot
Tips on Using the Platform
Additional Examples
Analyzing a Plackett-Burman Design
Analyzing a Supersaturated Design
Statistical Details
Performing Nonlinear Regression
Introduction to the Nonlinear Platform
Example of Nonlinear Fitting
Launch the Nonlinear Platform
Nonlinear Fitting with Fit Curve
Fit Curve Models and Options
Fit Curve Report
Model Options
Fit a Custom Model
Create a Model Column
Nonlinear Fit Report
Nonlinear Fit Options
Use the Model Library
Additional Examples
Maximum Likelihood: Logistic Regression
Probit Model with Binomial Errors: Numerical Derivatives
Poisson Loss Function
Statistical Details
Profile Likelihood Confidence Limits
How Custom Loss Functions Work
Notes Concerning Derivatives
Notes on Effective Nonlinear Modeling
Creating Neural Networks
Overview of Neural Networks
Launch the Neural Platform
The Neural Launch Window
The Model Launch
Model Reports
Training and Validation Measures of Fit
Confusion Statistics
Model Options
Example of a Neural Network
Modeling Relationships With Gaussian Processes
Launching the Platform
The Gaussian Process Report
Actual by Predicted Plot
Model Report
Marginal Model Plots
Platform Options
Borehole Hypercube Example
Fitting Dispersion Effects with the Loglinear Variance Model
Overview of the Loglinear Variance Model
Model Specification
Notes
Example Using Loglinear Variance
The Loglinear Report
Loglinear Platform Options
Save Columns
Row Diagnostics
Examining the Residuals
Profiling the Fitted Model
Example of Profiling the Fitted Model
Recursively Partitioning Data
Introduction to Partitioning
Launching the Partition Platform
Partition Method
Decision Tree
Bootstrap Forest
Boosted Tree
Validation
Graphs for Goodness of Fit
Actual by Predicted Plot
ROC Curve
Lift Curves
Missing Values
Example
Decision Tree
Bootstrap Forest
Boosted Tree
Compare Methods
Statistical Details
Performing Time Series Analysis
Launch the Platform
Time Series Commands
Graph
Autocorrelation
Partial Autocorrelation
Variogram
AR Coefficients
Spectral Density
Save Spectral Density
Number of Forecast Periods
Difference
Modeling Reports
Model Comparison Table
Model Summary Table
Parameter Estimates Table
Forecast Plot
Residuals
Iteration History
Model Report Options
ARIMA Model
Seasonal ARIMA
ARIMA Model Group
Transfer Functions
Report and Menu Structure
Diagnostics
Model Building
Transfer Function Model
Model Reports
Model Comparison Table
Fitting Notes
Smoothing Models
Performing Categorical Response Analysis
The Categorical Platform
Launching the Platform
Failure Rate Examples
Response Frequencies
Indicator Group
Multiple Delimited
Multiple Response By ID
Multiple Response
Categorical Reports
Report Content
Report Format
Statistical Commands
Save Tables
Performing Choice Modeling
Introduction to Choice Modeling
Choice Statistical Model
Product Design Engineering
Data for the Choice Platform
Example: Pizza Choice
Launch the Choice Platform and Select Data Sets
Choice Model Output
Subject Effects
Utility Grid Optimization
Platform Options
Example: Valuing Trade-offs
One-Table Analysis
Example: One-Table Pizza Data
Segmentation
Special Data Rules
Default choice set
Subject Data with Response Data
Logistic Regression
Transforming Data
Transforming Data to Two Analysis Tables
Transforming Data to One Analysis Table
Logistic Regression for Matched Case-Control Studies
Correlations and Multivariate Techniques
Launch the Multivariate Platform
Estimation Methods
The Multivariate Report
Multivariate Platform Options
Nonparametric Correlations
Scatterplot Matrix
Outlier Analysis
Item Reliability
Impute Missing Data
Examples
Example of Item Reliability
Computations and Statistical Details
Estimation Methods
Pearson Product-Moment Correlation
Nonparametric Measures of Association
Inverse Correlation Matrix
Distance Measures
Cronbach’s α
Clustering Data
Introduction to Clustering Methods
The Cluster Launch Dialog
Hierarchical Clustering
Hierarchical Cluster Options
Technical Details for Hierarchical Clustering
K-Means Clustering
K-Means Control Panel
K-Means Report
Normal Mixtures
Robust Normal Mixtures
Platform Options
Details of the Estimation Process
Self Organizing Maps
Analyzing Principal Components and Reducing Dimensionality
Principal Components
Launch the Platform
Report
Platform Options
Factor Analysis
Performing Discriminant Analysis
Introduction
Discriminating Groups
Discriminant Method
Stepwise Selection
Canonical Plot
Discriminant Scores
Commands and Options
Validation
Fitting Partial Least Squares Models
Overview of the Partial Least Squares Platform
Example of Partial Least Squares
Launch the Partial Least Squares Platform
Launch through Multivariate Methods
Launching through Fit Model
Centering and Scaling
Impute Missing Data
Model Launch Control Panel
The Partial Least Squares Report
Model Fit Options
Partial Least Squares Options
Statistical Details
Scoring Tests Using Item Response Theory
Introduction to Item Response Theory
Launching the Platform
Item Analysis Output
Characteristic Curves
Information Curves
Dual Plots
Platform Commands
Technical Details
Plotting Surfaces
Surface Plots
Launching the Platform
Plotting a Single Mathematical Function
Plotting Points Only
Plotting a Formula from a Column
Isosurfaces
The Surface Plot Control Panel
Appearance Controls
Independent Variables
Dependent Variables
Plot Controls and Settings
Rotate
Axis Settings
Lights
Sheet or Surface Properties
Other Properties and Commands
Keyboard Shortcuts
Visualizing, Optimizing, and Simulating Response Surfaces
Introduction to Profiling
The Profiler
Interpreting the Profiles
Profiler Options
Desirability Profiling and Optimization
Special Profiler Topics
Propagation of Error Bars
Customized Desirability Functions
Mixture Designs
Expanding Intermediate Formulas
Linear Constraints
Contour Profiler
Mixture Profiler
Surface Profiler
The Custom Profiler
The Simulator
Specifying Factors
Specifying the Response
Run the Simulation
The Simulator Menu
Using Specification Limits
Simulating General Formulas
The Defect Profiler
Noise Factors (Robust Engineering)
Profiling Models Stored in Excel
Running the JMP Profiler
Example of an Excel Model
Using the Excel Profiler From JMP
Fit Group
Statistical Details
Comparing Model Performance
Example of Model Comparison
Launch the Model Comparison Platform
The Model Comparison Report
Model Comparison Platform Options
Additional Example of Model Comparison
References
The Response Models
Continuous Responses
Nominal Responses
Ordinal Responses
The Factor Models
Continuous Factors
Nominal Factors
Ordinal Factors
The Usual Assumptions
Assumed Model
Relative Significance
Multiple Inferences
Validity Assessment
Alternative Methods
Key Statistical Concepts
Uncertainty, a Unifying Concept
The Two Basic Fitting Machines
Leverage Plot Details
Multivariate Details
Multivariate Tests
Approximate F-Test
Canonical Details
Discriminant Analysis
Power Calculations
Computations for the LSV
Computations for the LSN
Computations for the Power
Computations for Adjusted Power
Inverse Prediction with Confidence Limits
Details of Random Effects
Index
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Copyright Page
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Learn About JMP
Contents
JMP Modeling and Multivariate Methods
1 Learn About JMP
Documentation and Additional Resources
Book Conventions
JMP Documentation
JMP Documentation Suite
JMP Help
JMP Books by Users
JMPer Cable
Additional Resources for Learning JMP
Tutorials
The JMP Starter Window
Sample Data Tables
Learn about Statistical and JSL Terms
Learn JMP Tips and Tricks
Tooltips
Access Resources on the Web
2 Introduction to the Fit Model Platform
Creating Linear Models
Launch the Fit Model Platform
Example of the Fit Model Platform
Specify Different Model Types
Construct Model Effects
Add
Cross
Nest
Macros
Attributes
Transformations
Fitting Personalities
Emphasis Options for Standard Least Squares
Model Specification Options
Validity Checks
3 Fitting Standard Least Squares Models
Using the Fit Model Platform
Example Using Standard Least Squares
The Standard Least Squares Report and Options
Regression Reports
Summary of Fit
Analysis of Variance
Parameter Estimates
Effect Tests
Lack of Fit
Estimates
Show Prediction Expression
Sorted Estimates
Expanded Estimates
Indicator Parameterization Estimates
Sequential Tests
Custom Test
Joint Factor Tests
Inverse Prediction
Cox Mixtures
Parameter Power
Correlation of Estimates
Effect Screening
Scaled Estimates and the Coding Of Continuous Terms
Plot Options
Description of Lenth’s Method
Factor Profiling
The Profiler
Contour Profiler
Mixture Profiler
Surface Profiler
Interaction Plots
Cube Plots
Box Cox
Y
Transformations
Row Diagnostics
Leverage Plots
Save Columns
Effect Options
LSMeans Table
LSMeans Plot
LSMeans Contrast
LSMeans Student’s
t
and LSMeans Tukey’s HSD
Test Slices
Restricted Maximum Likelihood (REML) Method
Introduction to Random Effects
Generalizability
Unrestricted Parameterization for Variance Components in JMP
Negative Variances
Random Effects
BLUP
Parameters
REML and Traditional Methods Agree on the Standard Cases
F
-Tests in Mixed Models
Specifying Random Effects
REML Results
Method of Moments Results
Singularity Details
Examples with Statistical Details
One-Way Analysis of Variance with Contrasts
Analysis of Covariance
Analysis of Covariance with Separate Slopes
4 Fitting Stepwise Regression Models
Using the Fit Model Platform
Overview of Stepwise Regression
Example Using Stepwise Regression
The Stepwise Report
Stepwise Platform Options
Stepwise Regression Control Panel
Current Estimates Report
Step History Report
Models with Crossed, Interaction, or Polynomial Terms
Example Using Interaction Terms
Models with Nominal and Ordinal Terms
Example of a Model with a Nominal Term
Using the Make Model Command for Hierarchical Terms
Example Using the Make Model Command for Hierarchical Terms
Performing Logistic Stepwise Regression
Example Using Logistic Stepwise Regression
The All Possible Models Option
Example Using the All Possible Models Option
The Model Averaging Option
Example Using the Model Averaging Option
Using Validation
5 Fitting Multiple Response Models
Using the Fit Model Platform
Example of a Multiple Response Model
The Manova Report
The Manova Fit Options
Response Specification
Choose Response Options
Custom Test Option
Multivariate Tests
The Extended Multivariate Report
Comparison of Multivariate Tests
Univariate Tests and the Test for Sphericity
Multivariate Model with Repeated Measures
Repeated Measures Example
Example of a Compound Multivariate Model
Discriminant Analysis
Example of the Save Discrim Option
6 Fitting Generalized Linear Models
Using the Fit Model Platform
Overview of Generalized Linear Models
The Generalized Linear Model Personality
Examples of Generalized Linear Models
Model Selection and Deviance
Examples
Poisson Regression
Poisson Regression with Offset
Normal Regression, Log Link
Platform Commands
7 Performing Logistic Regression on Nominal and Ordinal Responses
Using the Fit Model Platform
Introduction to Logistic Models
The Logistic Fit Report
Logistic Plot
Iteration History
Whole Model Test
Lack of Fit Test (Goodness of Fit)
Parameter Estimates
Likelihood Ratio Tests
Logistic Fit Platform Options
Plot Options
Likelihood Ratio Tests
Wald Tests for Effects
Confidence Intervals
Odds Ratios (Nominal Responses Only)
Inverse Prediction
Save Commands
ROC Curve
Lift Curve
Confusion Matrix
Profiler
Validation
Example of a Nominal Logistic Model
Example of an Ordinal Logistic Model
Example of a Quadratic Ordinal Logistic Model
Stacking Counts in Multiple Columns
8 Analyzing Screening Designs
Using the Screening Platform
Overview of the Screening Platform
When to Use the Screening Platform
Comparing Screening and Fit Model
Launch the Screening Platform
The Screening Report
Contrasts
Half Normal Plot
Tips on Using the Platform
Additional Examples
Analyzing a Plackett-Burman Design
Analyzing a Supersaturated Design
Statistical Details
9 Performing Nonlinear Regression
Using the Nonlinear Platform
Introduction to the Nonlinear Platform
Example of Nonlinear Fitting
Launch the Nonlinear Platform
Nonlinear Fitting with Fit Curve
Fit Curve Models and Options
Fit Curve Report
Model Options
Fit a Custom Model
Create a Model Column
Nonlinear Fit Report
Nonlinear Fit Options
Use the Model
Library
Additional Examples
Maximum Likelihood: Logistic Regression
Probit Model with Binomial Errors: Numerical Derivatives
Poisson Loss Function
Statistical Details
Profile Likelihood Confidence Limits
How Custom Loss Functions Work
Notes Concerning Derivatives
Notes on Effective Nonlinear Modeling
10 Creating Neural Networks
Using the Neural Platform
Overview of Neural Networks
Launch the Neural Platform
The Neural Launch Window
The Model Launch
Model Reports
Training and Validation Measures of Fit
Confusion Statistics
Model Options
Example of a Neural Network
11 Modeling Relationships With Gaussian Processes
Using the Gaussian Process Platform
Launching the Platform
The Gaussian Process Report
Actual by Predicted Plot
Model Report
Marginal Model Plots
Platform Options
Borehole Hypercube Example
12 Fitting Dispersion Effects with the Loglinear Variance Model
Using the Fit Model Platform
Overview of the Loglinear Variance Model
Model Specification
Notes
Example Using Loglinear Variance
The Loglinear Report
Loglinear Platform Options
Save Columns
Row Diagnostics
Examining the Residuals
Profiling the Fitted Model
Example of Profiling the Fitted Model
13 Recursively Partitioning Data
Using the Partition Platform
Introduction to Partitioning
Launching the Partition Platform
Partition Method
Decision Tree
Bootstrap Forest
Boosted Tree
Validation
Graphs for Goodness of Fit
Actual by Predicted Plot
ROC Curve
Lift Curves
Missing Values
Example
Decision Tree
Bootstrap Forest
Boosted Tree
Compare Methods
Statistical Details
14 Performing Time Series Analysis
Using the Time Series Platform
Launch the Platform
Select Columns into Roles
The Time Series Graph
Time Series Commands
Graph
Autocorrelation
Partial Autocorrelation
Variogram
AR Coefficients
Spectral Density
Save Spectral Density
Number of Forecast Periods
Difference
Modeling Reports
Model Comparison Table
Model Summary Table
Parameter Estimates Table
Forecast Plot
Residuals
Iteration History
Model Report Options
ARIMA Model
Seasonal ARIMA
ARIMA Model Group
Transfer Functions
Report and Menu Structure
Diagnostics
Model Building
Transfer Function Model
Model Reports
Model Comparison Table
Fitting Notes
Smoothing Models
Simple Moving Average
Smoothing Model Dialog
Simple Exponential Smoothing
Double (Brown) Exponential Smoothing
Linear (Holt) Exponential Smoothing
Damped-Trend Linear Exponential Smoothing
Seasonal Exponential Smoothing
Winters Method (Additive)
15 Performing Categorical Response Analysis
Using the Categorical Platform
The Categorical Platform
Launching the Platform
Failure Rate Examples
Response Frequencies
Indicator Group
Multiple Delimited
Multiple Response By ID
Multiple Response
Categorical Reports
Report Content
Report Format
Statistical Commands
Save Tables
16 Performing Choice Modeling
Using the Choice Platform
Introduction to Choice Modeling
Choice Statistical Model
Product Design Engineering
Data for the Choice Platform
Example: Pizza Choice
Launch the Choice Platform and Select Data Sets
Choice Model Output
Subject Effects
Utility Grid Optimization
Platform Options
Example: Valuing Trade-offs
One-Table Analysis
Example: One-Table Pizza Data
Segmentation
Special Data Rules
Default choice set
Subject Data with Response Data
Logistic Regression
Transforming Data
Transforming Data to Two Analysis Tables
Transforming Data to One Analysis Table
Logistic Regression for Matched Case-Control Studies
17 Correlations and Multivariate Techniques
Using the Multivariate Platform
Launch the Multivariate Platform
Estimation Methods
The Multivariate Report
Multivariate Platform Options
Nonparametric Correlations
Scatterplot Matrix
Outlier Analysis
Item Reliability
Impute Missing Data
Examples
Example of Item Reliability
Computations and Statistical Details
Estimation Methods
Pearson Product-Moment Correlation
Nonparametric Measures of Association
Inverse Correlation Matrix
Distance Measures
Cronbach’s
α
18 Clustering Data
Using the Cluster Platform
Introduction to Clustering Methods
The Cluster Launch Dialog
Hierarchical Clustering
Hierarchical Cluster Options
Technical Details for Hierarchical Clustering
K-Means Clustering
K-Means Control Panel
K-Means Report
Normal Mixtures
Robust Normal Mixtures
Platform Options
Details of the Estimation Process
Self Organizing Maps
19 Analyzing Principal Components and Reducing Dimensionality
Using the Principal Components Platform
Principal Components
Launch the Platform
Report
Platform Options
Factor Analysis
20 Performing Discriminant Analysis
Using the Discriminant Platform
Introduction
Discriminating Groups
Discriminant Method
Stepwise Selection
Canonical Plot
Discriminant Scores
Commands and Options
Validation
21 Fitting Partial Least Squares Models
Using the Partial Least Squares Platform
Overview of the Partial Least Squares Platform
Example of Partial Least Squares
Launch the Partial Least Squares Platform
Launch through Multivariate Methods
Launching through Fit Model
Centering and Scaling
Impute Missing Data
Model Launch Control Panel
The Partial Least Squares Report
Model Fit Options
Partial Least Squares Options
Statistical Details
22 Scoring Tests Using Item Response Theory
Using the Item Analysis Platform
Introduction to Item Response Theory
Launching the Platform
Item Analysis Output
Characteristic Curves
Information Curves
Dual Plots
Platform Commands
Technical Details
23 Plotting Surfaces
Using the Surface Plot Platform
Surface Plots
Launching the Platform
Plotting a Single Mathematical Function
Plotting Points Only
Plotting a Formula from a Column
Isosurfaces
The Surface Plot Control Panel
Appearance Controls
Independent Variables
Dependent Variables
Plot Controls and Settings
Rotate
Axis Settings
Lights
Sheet or Surface Properties
Other Properties and Commands
Keyboard Shortcuts
24 Visualizing, Optimizing, and Simulating Response Surfaces
Using the Profiler Platforms
Introduction to Profiling
Profiling Features in JMP
The Profiler
Interpreting the Profiles
Profiler Options
Desirability Profiling and Optimization
Special Profiler Topics
Propagation of Error Bars
Customized Desirability Functions
Mixture Designs
Expanding Intermediate Formulas
Linear Constraints
Contour Profiler
Contour Profiler Pop-up Menu
Mixtures
Constraint Shading
Mixture Profiler
Explanation of Ternary Plot Axes
More than Three Mixture Factors
Mixture Profiler Options
Linear Constraints
Examples
Surface Profiler
The Custom Profiler
Custom Profiler Options
The Simulator
Specifying Factors
Specifying the Response
Run the Simulation
The Simulator Menu
Using Specification Limits
Simulating General Formulas
The Defect Profiler
Notes
Defect Profiler Example
Stochastic Optimization Example
Noise Factors (Robust Engineering)
Profiling Models Stored in Excel
Running the JMP Profiler
Example of an Excel Model
Using the Excel Profiler From JMP
Fit Group
Statistical Details
25 Comparing Model Performance
Using the Model Comparison Platform
Example of Model Comparison
Launch the Model Comparison Platform
The Model Comparison Report
Model Comparison Platform Options
Additional Example of Model Comparison
A Statistical Details
Models in JMP
The Response Models
Continuous Responses
Nominal Responses
Ordinal Responses
The Factor Models
Continuous Factors
Nominal Factors
Ordinal Factors
The Usual Assumptions
Assumed Model
Relative Significance
Multiple Inferences
Validity Assessment
Alternative Methods
Key Statistical Concepts
Uncertainty, a Unifying Concept
The Two Basic Fitting Machines
Leverage Plot Details
Multivariate Details
Multivariate Tests
Approximate
F
-Test
Canonical Details
Discriminant Analysis
Power Calculations
Computations for the LSV
Computations for the LSN
Computations for the Power
Computations for Adjusted Power
Inverse Prediction with Confidence Limits
Details of Random Effects
Index
JMP Modeling and Multivariate Methods
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