The Response Screening red triangle menu contains options to customize the display and to compute and save calculated data.
For selected relationships, adds the appropriate Fit Y by X reports to the Response Screening report. You can select relationships by selecting rows in the PValue data table or points in the plots.
For continuous Ys and categorical Xs, creates a data table with the counts, means, and standard deviations for each level of the categorical variable. If the Robust option is selected, the robust mean is included.
For continuous Ys and categorical Xs, tests all pairwise comparisons across the levels of the categorical variable. For each comparison, the data table gives the usual t-test, a test of practical significance, an equivalence test, and a column that uses color coding to summarize the results. The data table also contains a script that plots Practical LogWorth by Relative Practical Difference. See Compare Means Data Table. For an example, see Example of Tests of Practical Significance and Equivalence.
Saves a new group of columns to the original data table and places these in a column group call Residual Group. For each continuous Y and categorical X, a column is constructed containing the residuals divided by their estimated standard deviation. In other words, the column contains standardized residuals. The column is defined by a formula.
If the Robust option is selected, standardized residual columns are constructed using robust fits and robust estimates.
Saves a new group of columns to the original data table and places these in a column group call Outlier Group. Save Outlier Indicator is most effective when you have selected the Robust option.
For each continuous Y and categorical X, a column that indicates outliers is constructed. An outlier is a point whose distance to the predicted value exceeds three times an estimate of sigma. In other words, an outlier is a point whose standardized residual exceeds three. The column is defined by a formula.
If the Robust option is selected, robust fits and robust estimates are used. An outlier is a point whose distance to the predicted value exceeds three times the robust estimate of sigma.
The Cluster Outliers script is added to the original data table. The script shows outliers on a hierarchical cluster plot of the data.
Lists the Script menu options for the platform. See the Using JMP book for details.
Lists the Script All By-Group menu options for the platform. See the Using JMP book for details.
The Means data table contains a row for each combination of response and X level. For the Probe.jmp sample data table, there are 387 response variables, each tested against Process at two levels. The Means table contains 387x2 = 774 rows (Means Data Table).
When your data table consists of a large number of rows (large n), the standard error used in testing can be very small. As a result, tests might be statistically significant, when in fact, the observed difference is too small to be of practical consequence. Tests of practical significance enable you to specify the size of the difference that you consider worth detecting. This difference is called the practical difference. Instead of testing that the difference is zero, you test whether the difference exceeds the practical difference. As a result, the tests are more meaningful, and fewer tests need to be scrutinized.
Equivalence tests enable you to determine whether two levels have essentially the same effect, from a practical perspective, on the response. In other words, an equivalence test tests whether the difference is smaller than the practical difference.
The Compare Means data table provides results for both tests of practical difference and tests of practical equivalence. Each row compares a response across two levels of a categorical factor. Results of the pairwise comparisons are color-coded to facilitate interpretation. See Practical Difference for a description of how the practical difference is specified. See Example of Tests of Practical Significance and Equivalence for an example.
The Compare Means data table contains a script that plots Practical LogWorth by Relative Practical Difference. Relative Practical Difference is defined as the actual difference divided by the practical difference.
The estimated difference in means across the two levels. If the Robust option is selected, robust estimates of the means are used.
The standard error of the difference in means. This is a robust estimate if the Robust option is selected.
The p-value for the usual Student's t-test for a pairwise comparison. This is the robust version of the t-test when the Robust option is selected. Tests that are significant at the 0.05 level are highlighted.
The difference in means that is considered to be of practical interest. If you assign a Spec Limit property to the Y variable, the practical difference is computed as the difference between the specification limits multiplied by the Practical Difference Proportion. If no Practical Difference Proportion has been specified, the Practical Difference is the difference between the specification limits multiplied by 0.10.
If you do not assign a Spec Limit property to the Y variable, an estimate of its standard deviation is computed from its interquartile range (IQR). This estimate is . The Practical Difference is computed as multiplied by the Practical Difference Proportion. If no Practical Difference Proportion has been specified, the Practical Difference is computed as multiplied by 0.10.
The p-value for a test of whether the absolute value of the mean difference in Y between Leveli and Levelj is less than or equal to the Practical Difference. A small p-value indicates that the absolute difference exceeds the Practical Difference. This indicates that Leveli and Levelj account for a difference that is of practical consequence.
Uses the Two One-Sided Tests (TOST) method to test for a practical difference between the means (Schuirmann, 1987). The Practical Difference specifies a threshold difference for which smaller differences are considered practically equivalent. One-sided t tests are constructed for two null hypotheses: the true difference exceeds the Practical Difference; the true difference is less than the negative of the Practical Difference. If both tests reject, this indicates that the absolute difference in the means falls within the Practical Difference. Therefore, the groups are considered practically equivalent.
The Practical Equivalence PValue is the largest p-value obtained on the one-sided t tests. A small Practical Equiv PValue indicates that the mean response for Leveli is equivalent, in a practical sense, to the mean for Levelj.
A description of the results of the tests for practical difference and equivalence. Values are color-coded to help identify significant results.