Response screening is available as a platform and as a Fit Model personality. In both cases, it performs tests analogous to those found in the Fit Y by X platform, as shown in Table 18.1. As a personality, it performs tests of the response against the individual model effects.
Because you are conducting a large number of tests, you need to control the overall rate of declaring tests significant by chance alone. Response screening controls the false discovery rate. The False Discovery Rate (FDR) is the expected proportion of significant tests that are incorrectly declared significant (Benjamini and Hochberg 1995; Westfall et al. 2011).
When you have many observations, even small effects that are of no practical consequence can result in statistical significance. To address this issue, you can define an effect size that you consider to be of practical significance. You then conduct tests of practical significance, thereby only detecting effects large enough to be of pragmatic interest.
The JSL command Summarize Y by X performs the same function as the Response Screening platform but without creating a platform window. See Summarize YByX(X(<x columns>, Y (<y columns>), Group(<grouping columns>), Freq(<freq column>), Weight(<weight column>)) in the JSL Syntax Reference book for details.