Loglinear Variance Models

Model the Variance and the Mean of the Response

The Loglinear Variance personality of the Fit Model platform enables you to model both the expected value and the variance of a response using regression models. The log of the variance is fit to one linear model and the expected response is fit to a different linear model simultaneously.

Note: The estimates are demanding in their need for a lot of well-designed, well-fitting data. You need more data to fit variances than you do means.

For many engineers, the goal of an experiment is not to maximize or minimize the response itself, but to aim at a target response and achieve minimum variability. The loglinear variance model provides a very general and effective way to model variances, and can be used for unreplicated data, as well as data with replications.

Contents

Overview of the Loglinear Variance Model

Example Using Loglinear Variance

Launch the Loglinear Variance Personality

The Loglinear Variance Fit Report

Loglinear Variance Fit Report Options

Additional Examples of Loglinear Variance Models

Example of Examining the Residuals in a Loglinear Variance Model

Example of Profiling a Fitted Loglinear Variance Model

Want more information? Have questions? Get answers in the JMP User Community (community.jmp.com).