Loglinear Variance Models

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Fitting Linear Models • Loglinear Variance Models

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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

Dispersion Effects

Model Specification

Example Using Loglinear Variance

The Loglinear Report

Loglinear Platform Options

Row Diagnostics

Examining the Residuals

Profiling the Fitted Model

Example of Profiling the Fitted Model

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Help created on 7/12/2018