JMP 14.2 Online Documentation (English)
Discovering JMP
Using JMP
Basic Analysis
Essential Graphing
Profilers
Design of Experiments Guide
Fitting Linear Models
Predictive and Specialized Modeling
Multivariate Methods
Quality and Process Methods
Reliability and Survival Methods
Consumer Research
Scripting Guide
JSL Syntax Reference
JMP iPad Help
JMP Interactive HTML
Capabilities Index
JMP 13.2 Online Documentation
Fitting Linear Models
• Model Specification
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Model Specification
Specify Linear Models
Using the Fit Model platform, you can specify complex models efficiently. Your task is simplified by Macros, Attributes, and transformations. Fit Model is your gateway to fitting a broad variety of models and effect structures.
These include:
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simple and multiple linear regression
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analysis of variance and covariance
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random effect, nested effect, mixed effect, repeated measures, and split plot models
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nominal and ordinal logistic regression
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multivariate analysis of variance (MANOVA)
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canonical correlation and discriminant analysis
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loglinear variance (to model the mean and the variance)
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generalized linear models (GLM)
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parametric survival and proportional hazards
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response screening, for studying a large number of responses
In JMP Pro, you can also fit the following:
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mixed models with a range of covariance structures
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generalized regression models including the elastic net, lasso, and ridge regression
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partial least squares
The Fit Model platform lets you fit a large variety of types of models by selecting the desired personality. This chapter focuses on the elements of the Model Specification window that are common to most personalities.
Contents
Overview of the Fit Model Platform
Example of a Regression Analysis Using Fit Model
Launch the Fit Model Platform
Fit Model Launch Window Elements
Construct Model Effects
Fitting Personalities
Model Specification Options
Informative Missing
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
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Help created on 1/2/2019