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by SAS Institute
JMP 13 Fitting Linear Models, Second Edition, 2nd Edition
Contents
Learn about JMP
Documentation and Additional Resources
Formatting Conventions
JMP Documentation
JMP Documentation Library
JMP Help
Additional Resources for Learning JMP
Tutorials
Sample Data Tables
Learn about Statistical and JSL Terms
Learn JMP Tips and Tricks
Tooltips
JMP User Community
JMPer Cable
JMP Books by Users
The JMP Starter Window
Technical Support
Model Specification
Specify Linear Models
Overview of the Fit Model Platform
Example of a Regression Analysis Using Fit Model
Launch the Fit Model Platform
Fit Model Launch Window Elements
Frequency
Weight
Construct Model Effects
Add
Cross
Nest
Macros
Attributes
Transform
No Intercept
Construct Model Effects Tabs
Fitting Personalities
Model Specification Options
Informative Missing
Continuous Effects
Categorical Effects
Coding Table
Validity Checks
Examples of Model Specifications and Their Model Fits
Simple Linear Regression
Polynomial in X to Degree k
Polynomial in X and Z to Degree k
Multiple Linear Regression
One-Way Analysis of Variance
Two-Way Analysis of Variance
Two-Way Analysis of Variance with Interaction
Three-Way Full Factorial
Analysis of Covariance, Equal Slopes
Analysis of Covariance, Unequal Slopes
Two-Factor Nested Random Effects Model
Three-Factor Fully Nested Random Effects Model
Simple Split Plot or Repeated Measures Model
Two-Factor Response Surface Model
Knotted Spline Effect
Standard Least Squares Report and Options
Analyze Common Classes of Models
Example Using Standard Least Squares
Launch the Standard Least Squares Personality
Fit Model Launch Window
Fixed Effects Only
Random Effects
Standard Least Squares Options in the Fit Model Launch Window
Emphasis Options for Standard Least Squares
Validation
Missing Values
Fit Least Squares Report
Single versus Multiple Responses
Report Structure Related to Emphasis
Special Reports
Singularity Details
Response Surface Report
Mixed and Random Effect Model Reports
Crossvalidation Report
Least Squares Fit Options
Fit Group Options
Response Options
Regression Reports
Summary of Fit
Analysis of Variance
Parameter Estimates
Effect Tests
Effect Details
Table of Effect Options
LSMeans Table
LSMeans Plot
LSMeans Contrast
LSMeans Student’s t and LSMeans Tukey HSD
LSMeans Dunnett
Test Slices
Power Analysis
Lack of Fit
Estimates
Show Prediction Expression
Sorted Estimates
Sorted Estimates Report for Saturated Models
Expanded Estimates
Interpretation of Tests for Expanded Estimates
Indicator Parameterization Estimates
Sequential Tests
Custom Test
Custom Test Report Components
Custom Test Report Options
Multiple Comparisons
Launch the Option
Comparisons with Overall Average
Comparisons with Control
All Pairwise Comparisons
Joint Factor Tests
Inverse Prediction
Cox Mixtures
Parameter Power
Correlation of Estimates
Coding for Nominal Effects
Effect Screening
Scaled Estimates and the Coding of Continuous Terms
Plot Options
Transformations
Lenth PSE Values
Parameter Estimate Population Report
Correlations of Estimates Report
“Transformation to make uncorrelated” Report
Normal Plot Report
Bayes Plot Report
Pareto Plot Report
Factor Profiling
Profiler
Interaction Plots
Contour Profiler
Mixture Profiler
Cube Plots
Box Cox Y Transformation
Surface Profiler
Row Diagnostics
Leverage Plots
Construction
Confidence Curves
X Axis Scaling
Leverage
Multicollinearity
The Whole Model Actual by Predicted Plot
Press
Save Columns
Prediction Formula
Effect Summary Report
Effect Summary Table Columns
Effect Summary Table Options
Effect Heredity
Multiple Responses
Mixed and Random Effect Model Reports and Options
Mixed Models and Random Effect Models
Random Effects
The Classical Linear Mixed Model
REML versus EMS for Fitting Models with Random Effects
Specifying Random Effects and Fitting Method
Unrestricted Parameterization for Variance Components
Negative Variances
Restricted Maximum Likelihood (REML) Method
Random Effect Predictions
REML Variance Component Estimates
Covariance Matrix of Variance Components Estimates
Iterations
Fixed Effect Tests
REML Save Columns Options
REML Profiler Option
EMS (Traditional) Model Fit Reports
Expected Mean Squares
Variance Component Estimates
Test Denominator Synthesis
Tests wrt Random Effects
EMS Profiler
Models with Linear Dependencies among Model Terms
Singularity Details
Parameter Estimates Report
Effect Tests Report
Examples
Statistical Details
Emphasis Rules
Details of Custom Test Example
Correlation of Estimates
Leverage Plot Details
Construction
Superimposing a Test on the Leverage Plot
The Kackar-Harville Correction
Degrees of Freedom
Power Analysis
Effect Size
Effect Size and Power
Plot of Power by Sample Size
The Least Significant Number (LSN)
The Least Significant Value (LSV)
Power
The Adjusted Power and Confidence Intervals
Example of Retrospective Power Analysis
Prospective Power Analysis
Standard Least Squares Examples
Analyze Common Classes of Models
One-Way Analysis of Variance Example
Analysis of Covariance with Equal Slopes Example
Analysis of Covariance with Unequal Slopes Example
Response Surface Model Example
Fit the Full Response Surface Model
Reduce the Model
Examine the Response Surface Report
Find the Critical Point Using the Prediction Profiler
View the Surface Using the Contour Profiler
Split Plot Design Example
Estimation of Random Effect Parameters Example
Knotted Spline Effect Example
Bayes Plot for Active Factors Example
Stepwise Regression Models
Find a Model Using Variable Selection
Overview of Stepwise Regression
Example Using Stepwise Regression
The Stepwise Report
Stepwise Platform Options
Stepwise Regression Control Panel
Stopping Rule
Direction
Go, Stop, Step Buttons
Rules
Buttons
Statistics
Forward Selection Example
Backward Selection Example
Current Estimates Report
Step History Report
Models with Crossed, Interaction, or Polynomial Terms
Example of the Combine Rule
Models with Nominal and Ordinal Effects
Construction of Hierarchical Terms
Example of a Model with a Nominal Term
Construction of Hierarchical Terms in Example
Example of the Restrict Rule for Hierarchical Terms
Performing Binary and Ordinal 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
Validation Set with Two or Three Values
Max Validation RSquare
Validation and Test Set Statistic Definitions
K-Fold Cross Validation
RSquare K-Fold Statistic
Max K-Fold RSquare
Generalized Regression Models
Build Models Using Variable Selection Techniques
Generalized Regression Overview
Example of Generalized Regression
Launch the Generalized Regression Personality
Distribution
Continuous
Discrete
Zero-Inflated
Generalized Regression Report Window
Generalized Regression Report Options
Model Launch Control Panel
Estimation Method Options
Advanced Controls
Validation Method Options
Early Stopping
Go
Model Fit Reports
Model Summary
Model Description Detail
Model Fit Detail
Estimation Details
Solution Path
Current Model Indicator
Solution Path Plot
The Solution ID
Validation Plot
Comparable Model Zones
Parameter Estimates for Centered and Scaled Predictors
Parameter Estimates for Original Predictors
Active Parameter Estimates
Effect Tests
Model Fit Options
Statistical Details
Statistical Details for Estimation Methods
Ridge Regression
Lasso Regression
Elastic Net
Adaptive Methods
Statistical Details for Advanced Controls
Grid
Statistical Details for Distributions
Continuous Distributions
Discrete Distributions
Zero-Inflated Distributions
Generalized Regression Examples
Build Models Using Regularization Techniques
Poisson Generalized Regression Example
Binomial Generalized Regression Example
Zero-Inflated Poisson Regression Example
Mixed Models
Jointly Model the Mean and Covariance
Overview of the Mixed Model Personality
Example Using Mixed Model
Launch the Mixed Model Personality
Fit Model Launch Window
Fixed Effects Tab
Random Effects Tab
Repeated Structure Tab
The Fit Mixed Report
Fit Statistics
Convergence Score Test
Random Effects Covariance Parameter Estimates
Confidence Intervals for Variance Components
Fixed Effects Parameter Estimates
Repeated Effects Covariance Parameter Estimates
Random Coefficients
Random Effects Predictions
Fixed Effects Tests
Multiple Comparisons
Marginal Model Inference
Actual by Predicted Plot
Residual Plots
Profilers
Conditional Model Inference
Actual by Conditional Predicted Plot
Conditional Residual Plots
Conditional Profilers
Variogram
Nugget
Variogram Options
Save Columns
Additional Examples
Repeated Measures Example
Background
Covariance Structures
Data Structure
Covariance Structure: Unstructured
Covariance Structure: Residual
Covariance Structure: Toeplitz
Covariance Structure: AR(1)
Further Analysis Using AR(1) Structure
Regression Model for AR(1) Model Example
Split Plot Example
Spatial Example: Uniformity Trial
Fit a Spatial Structure Model
Fit the Independent Errors Model
Conduct a Likelihood Ratio Test (Optional)
Select the Type of Spatial Covariance
Determine the Type of the Spatial Structure
Compare the Model to Block Designs
Correlated Response Example
Fit Univariate Models
Perform Mixed Model Analysis
Explore the Layout by Characteristic Interaction with the Profiler
Plot of Y by Layout and by Quadrant
Statistical Details
Convergence Score Test
Score Test
Relative Gradient
Random Coefficient Model
Repeated Measures
Repeated Covariance Structures
Unequal Variances Covariance Structure
Unstructured Covariance Structure
Compound Symmetry Covariance Structure
AR(1) Covariance Structure
Toeplitz Covariance Structure
Antedependent Covariance Structure
Spatial and Temporal Variability
Spatial Correlation Structure
Variogram
Variogram Estimate
Empirical Semivariance
The Kackar-Harville Correction
Degrees of Freedom
Multivariate Response Models
Fit Relationships Using MANOVA
Example of a Multiple Response Model
The Manova Report
The Manova Fit Options
Response Specification
Choose Response Options
Custom Test Option
Test Details
Centroid Plot
Save Canonical Scores
Canonical Correlation
Multivariate Tests
The Extended Multivariate Report
Comparison of Multivariate Tests
Univariate Tests and the Test for Sphericity
Example of Univariate and Sphericity Test
Multivariate Model with Repeated Measures
Repeated Measures Example
Example of a Compound Multivariate Model
Discriminant Analysis
Example of the Save Discrim Option
Statistical Details
Multivariate Tests
Approximate F-Tests
Canonical Details
Loglinear Variance Models
Model the Variance and the Mean of the Response
Overview of the Loglinear Variance Model
Dispersion Effects
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
Logistic Regression Models
Fit Regression Models for Nominal or Ordinal Responses
Logistic Regression Overview
Nominal Logistic Regression
Ordinal Logistic Regression
Other JMP Platforms That Fit Logistic Regression Models
Examples of Logistic Regression
Example of Nominal Logistic Regression
Example of Ordinal Logistic Regression
Launch the Nominal Logistic and Ordinal Logistic Personalities
Validation
The Logistic Fit Report
Whole Model Test
Fit Details
Lack of Fit Test
Logistic Fit Platform Options
Options for Nominal and Ordinal Fits
Options for Nominal Fits
Options for Ordinal Fits
Additional Examples of Logistic Regression
Example of Inverse Prediction
Example of Using Effect Summary for a Nominal Logistic Model
Example of a Quadratic Ordinal Logistic Model
Example of Stacking Counts in Multiple Columns
Statistical Details
Logistic Regression Model
Odds Ratios
Relationship of Statistical Tests
Generalized Linear Models
Fit Models for Nonnormal Response Distributions
Generalized Linear Models Overview
Example of a Generalized Linear Model
Launch the Generalized Linear Model Personality
Generalized Linear Model Fit Report
Whole Model Test
Generalized Linear Model Fit Report Options
Additional Examples of the Generalized Linear Models Personality
Using Contrasts to Compare Differences in the Levels of a Variable
Poisson Regression with Offset
Normal Regression with a Log Link
Statistical Details
Model Selection and Deviance
Statistical Details
Fitting Linear Models
The Response Models
Continuous Responses
Fitting Principle for Continuous Response
Base Model for Continuous Responses
Nominal Responses
Fitting Principle For Nominal Response
Base Model for Nominal Responses
Ordinal Responses
Fitting Principle For Ordinal Response
Base Model
The Factor Models
Continuous Factors
Nominal Factors
Interpretation of Parameters
Interactions and Crossed Effects
Nested Effects
Least Squares Means across Nominal Factors
Effective Hypothesis Tests
Singularities and Missing Cells in Nominal Effects
Ordinal Factors
Ordinal Interactions
Hypothesis Tests for Ordinal Crossed Models
Ordinal Least Squares Means
Singularities and Missing Cells in Ordinal Effects
Example with Missing Cell
Frequencies
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
Springs
Pressure Cylinders
Likelihood, AICc, and BIC
Power Calculations
Computations for the LSN
Computations for the LSV
Computations for the Power
Computations for the Adjusted Power
Inverse Prediction with Confidence Limits
References
Index
Fitting Linear Models
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Multivariate Response Models
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Example of a Multiple Response Model
Contents
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
Statistical Details
Multivariate Tests
Approximate F-Tests
Canonical Details
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