Publication date: 07/30/2020

This report gives details about parameter estimates, fit criteria, and the fitting algorithm.

Figure 5.15 shows the Parameter Estimates report obtained by running the script MaxDiff for Flavor in Potato Chip Responses.jmp.

Figure 5.15 Parameter Estimates Report

Term

Lists the terms in the model.

Estimate

An estimate of the parameter associated with the corresponding term. In discrete choice experiments, parameter estimates are sometimes referred to as part-worths. Each part-worth is the coefficient of utility associated with the given term. By default, these estimates are based on the Firth bias-corrected maximum likelihood estimators and therefore are considered to be more accurate than MLEs without bias correction.

Std Error

An estimate of the standard deviation of the parameter estimate.

The following fit statistics are shown as part of the report and can be used to compare models: AICc (corrected Akaike’s Information Criterion), BIC (Bayesian Information Criterion), −2Loglikelihood, and −2Firth Loglikelihood. See Statistical Details in Fitting Linear Models for more information about the first three of these measures.

The −2Firth Loglikelihood value is included in the report only when the Firth Bias-adjusted Estimates check box is checked in the launch window. This option is checked by default.

For each of these statistics, a smaller value indicates a better fit.

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