The Probe.jmp sample data table contains 387 characteristics (the Responses column group) measured on 5800 wafers. The Lot ID and Wafer Number columns uniquely identify the wafer. You are interested in which of the characteristics show different values across a process change (Process).
1.
Open the Probe.jmp sample data table.
2.
Select Analyze > Modeling > Response Screening.
3.
Select the Responses column group and click Y, Response.
4.
Select Process and click X.
5.
The Response Screening report appears, along with a data table of supporting information. The report (Response Screening Report for 387 Tests against Process) shows the FDR PValue Plot, but also contains two other plot reports. The table contains a row for each of the 387 columns that you entered as Y, Response.
The FDR PValue Plot shows two types of p-values, FDR PValue and PValue, for each of the 387 tests. These are plotted against Rank Fraction. PValue is the usual p-value for the test of a Y against Process. The FDR PValue is a p-value that is adjusted to guarantee a given false discover rate (FDR), here 0.05. The FDR PValues are plotted in blue and the PValues are plotted in red. The Rank Fraction ranks the FDR p-values from smallest to largest, in order of decreasing significance.
Both the horizontal blue line and the sloped red line on the plot are thresholds for FDR significance. Tests with FDR p-values that fall below the blue line are significant at the 0.05 level when adjusted for the false discovery rate. Tests with ordinary p-values that fall below the red line are significant at the 0.05 level when adjusted for the false discovery rate. In this way, the plot enables you to read FDR significance from either set of p-values.
Response Screening Report for 387 Tests against Process
The FDR PValue Plot shows that more than 60% of the tests are significant. A handful of tests are significant using the usual p-value, but not significant using the FDR p-value. These tests correspond to the red points that are above the red line, but below the blue line.
To identify the characteristics that are significantly different across Process, you can drag a rectangle around the appropriate points in the plot. This selects the rows corresponding to these points in the PValues table, where the names of the characteristics are given in the first column. Alternatively, you can select the corresponding rows in the PValues table.
The PValues data table (PValues Data Table, Partial View) contains 387 rows, one for each response measure in the Responses group. The response is given in the first column, called Y. Each response is tested against the effect in the X column, namely, Process.
The remaining columns give information about the test of Y against X. Here the test is a Oneway Analysis of Variance. In addition to other information, the table gives the test’s p-value, LogWorth, FDR (False Discovery Rate) p-value, and FDR LogWorth. See The Response Screening Report for details. Use this table to sort by the various statistics, select rows, or plot quantities of interest.
See The Response Screening Report for details about the report and PValues table.
PValues Data Table, Partial View