Publication date: 07/30/2020

If you suspect that your data contains outliers, select the Robust option on the launch window to reduce the sensitivity of tests for continuous responses to outliers. With this option, Huber M-estimates (Huber and Ronchetti 2009) are used in fitting regression and ANOVA models. Huber M-estimates are fairly close to least squares estimates when there are no outliers, but use outlier-downweighting when there are outliers.

The following columns are added to the PValues data table when the Robust option is selected in the launch window. The Robust option applies only when Y is continuous, so Robust column cells are empty when Y is categorical. See Fit Robust in Basic Analysis for more information about Huber M-estimation. For an example, see Example of Robust Fit.

Robust PValue

The p-value for the significance test corresponding to the pair of Y and X variables using a robust.

Robust LogWorth

The quantity -log10(Robust PValue).

Robust FDR PValue

The False Discovery Rate calculated for the Robust PValues using the Benjamini-Hochberg technique. If there is no Group variable, the multiple test adjustment applies to all tests displayed in the table. If there is a Group variable, the multiple test adjustment applies to all tests conducted for each level of the Group variable.

Robust FDR LogWorth

The quantity -log10(Robust FDR PValue).

Robust Rank Fraction

The rank of the Robust FDR LogWorth expressed as a fraction of the number of tests.

Robust Chisq

The chi-square value associated with the robust test.

Robust Sigma

The robust estimate of the error standard deviation.

Robust Outlier Portion

The portion of the values whose distance from the robust mean exceeds three times the Robust Sigma.

Robust CpuTime

Time in seconds required to create the Robust report.

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