The PValues data table contains a row for each pair of Y and X variables. If you specified a column for Group, the PValues data table contains a first column called Group. A row appears for each level of the Group column and for each pair of Y and X variables.
PValues Data Table, Partial View shows the PValues data table created using the Probe.jmp sample data table.
The PValues data table displays columns containing measures and statistics that are appropriate for the selected fit and combination of Y and X modeling types. The columns in the data table include:
The p-value for the significance test corresponding to the pair of Y and X variables. See the Basic Analysis book for additional details about Fit Y by X statistics.
The quantity -log10(p-value). This transformation adjusts p-values to provide an appropriate scale for graphing. A value that exceeds 2 is significant at the 0.01 level (because ).
The False Discovery Rate p-value calculated using the Benjamini-Hochberg technique. This technique adjusts the p-values to control the false discovery rate for multiple tests. If there is no Group variable, the set of multiple tests includes all tests displayed in the table. If there is a Group variable, the set of multiple tests consists of all tests conducted for each level of the Group variable. For details about the FDR correction, see Benjamini and Hochberg, 1995. For details about the false discovery rate, see The False Discovery Rate.
The quantity -log10(FDR PValue). This is the best statistic for plotting and assessing significance. Note that small p-values result in high FDR LogWorth values.
Indicates the extent to which response values differ across the levels or values of X. Effect sizes are scale invariant.
The rank of the FDR LogWorth expressed as a fraction of the number of tests. If the number of tests is m, the largest FDR LogWorth value has Rank Fraction 1/m, and the smallest has Rank Fraction 1. Equivalently, the Rank Fraction ranks the p-values in increasing order, as a fraction of the number of tests. The Rank Fraction is used in plotting the PValues and FDR PValues in rank order of decreasing significance.
Appears when Y is continuous. The coefficient of determination, which measures the proportion of total variation explained by the model.
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 only applies when Y is continuous, so Robust column cells are empty when Y is categorical. See the Basic Analysis book for additional details about Huber M-estimation. For an example, see Example of Robust Fit.
The p-value for the significance test corresponding to the pair of Y and X variables using a robust.
The quantity -log10(Robust 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.
The quantity -log10(Robust FDR PValue).