Book description
JMP 10 Modeling and Multivariate Methods begins by showing you how to take advantage of classic modeling techniques such as linear, nonlinear, and mixed models. The book continues with discussions on neural networking, time series analysis, multivariate techniques, and stepwise regression along with many other JMP modeling and multivariate methods. Examples guide you through each analysis, and statistical references and algorithms are included.
Table of contents
 Cover Page
 Title Page
 Copyright Page
 Contents
 Learn About JMP
 Introduction to the Fit Model Platform

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
 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
 Fitting Generalized Linear Models
 Performing Logistic Regression on Nominal and Ordinal Responses
 Analyzing Screening Designs
 Performing Nonlinear Regression
 Creating Neural Networks
 Modeling Relationships With Gaussian Processes
 Fitting Dispersion Effects with the Loglinear Variance Model
 Recursively Partitioning Data
 Performing Time Series Analysis
 Performing Categorical Response Analysis

Performing Choice Modeling
 Introduction to Choice Modeling
 Product Design Engineering
 Data for the Choice Platform
 Example: Pizza Choice
 Launch the Choice Platform and Select Data Sets
 Platform Options
 Example: Valuing Tradeoffs
 OneTable Analysis
 Segmentation
 Special Data Rules
 Transforming Data
 Logistic Regression for Matched CaseControl Studies
 Correlations and Multivariate Techniques
 Clustering Data
 Analyzing Principal Components and Reducing Dimensionality
 Performing Discriminant Analysis
 Fitting Partial Least Squares Models
 Scoring Tests Using Item Response Theory
 Plotting Surfaces
 Visualizing, Optimizing, and Simulating Response Surfaces
 Comparing Model Performance
 References
 Statistical Details
 Index
Product information
 Title: JMP 10 Modeling and Multivariate Methods
 Author(s):
 Release date: March 2012
 Publisher(s): SAS Institute
 ISBN: 9781612901985
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