Book description
JMP 11 Fitting Linear Models focuses on the Fit Model platform and many of its personalities. Linear and logistic regression, analysis of variance and covariance, and stepwise procedures are covered. Also included are multivariate analysis of variance, mixed models, generalized models, and models based on penalized regression techniques.
Table of contents
- Contents
- Learn about JMP
-
Introduction to Fit Model
- Specify Linear Models
- Overview of the Fit Model Platform
- Example of a Regression Analysis Using Fit Model
- Launch the Fit Model Platform
- Construct Model Effects
- Model Specification Options
- 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
- Statistical Details
-
Standard Least Squares Report and Options
- Analyze Common Classes of Models
- Example Using Standard Least Squares
- Launch the Standard Least Squares Personality
- Fit Least Squares Report
- Response Options
- Regression Reports
- Estimates
- Effect Screening
- Factor Profiling
- Row Diagnostics
- Save Columns
- Mixed and Random Effect Model Reports and Options
- Models with Linear Dependencies among Model Terms
- Statistical Details
-
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
- 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
- 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
-
Generalized Regression Models
- Build Models Using Regularization Techniques
- Generalized Regression Overview
- Example of Generalized Regression
- Launch the Generalized Regression Personality
- Model Fit Reports
- Model Fit Options
- Generalized Regression Options
- Additional Examples of the Generalized Regression Personality
- Statistical Details
-
Mixed Models
- Jointly Model the Mean and Covariance
- Overview of the Mixed Model Personality
- Example Using Mixed Model
- Launch the Mixed Model Personality
- The Fit Mixed Report
- Additional Examples
- Statistical Details
- Multivariate Response Models
- Loglinear Variance Models
- Logistic Regression with Nominal or Ordinal Responses
- Generalized Linear Models
- References
- Statistical Details
- Index
Product information
- Title: JMP 11 Fitting Linear Models
- Author(s):
- Release date: September 2013
- Publisher(s): SAS Institute
- ISBN: 9781612906768
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