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
Notes
Example Using Loglinear Variance
The Loglinear Report
Loglinear Platform Options
Save Columns
Row Diagnostics
Examining the Residuals
Profiling the Fitted Model