Book description
JMP 13 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

Model Specification
 Specify Linear Models
 Overview of the Fit Model Platform
 Example of a Regression Analysis Using Fit Model
 Launch the Fit Model Platform
 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
 OneWay Analysis of Variance
 TwoWay Analysis of Variance
 TwoWay Analysis of Variance with Interaction
 ThreeWay Full Factorial
 Analysis of Covariance, Equal Slopes
 Analysis of Covariance, Unequal Slopes
 TwoFactor Nested Random Effects Model
 ThreeFactor Fully Nested Random Effects Model
 Simple Split Plot or Repeated Measures Model
 TwoFactor 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 Least Squares Report
 Response Options
 Regression Reports
 Estimates
 Effect Screening
 Factor Profiling
 Row Diagnostics
 Save Columns
 Effect Summary Report
 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
 OneWay 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 Effects
 Performing Binary and Ordinal Logistic Stepwise Regression
 The All Possible Models Option
 The Model Averaging Option
 Using Validation

Generalized Regression Models
 Build Models Using Variable Selection Techniques
 Generalized Regression Overview
 Example of Generalized Regression
 Launch the Generalized Regression Personality
 Generalized Regression Report Window
 Model Launch Control Panel
 Model Fit Reports
 Model Fit Options
 Statistical Details
 Generalized Regression Examples

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
 Multiple Comparisons
 Marginal Model Inference
 Conditional Model Inference
 Save Columns
 Additional Examples
 Statistical Details
 Multivariate Response Models
 Loglinear Variance Models
 Logistic Regression Models

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
 Generalized Linear Model Fit Report Options
 Additional Examples of the Generalized Linear Models Personality
 Statistical Details
 Statistical Details
 References
 Index
Product information
 Title: JMP 13 Fitting Linear Models, Second Edition, 2nd Edition
 Author(s):
 Release date: February 2017
 Publisher(s): SAS Institute
 ISBN: 9781629609522
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