A data scientist uses PROC HPREG with SELECTION=STEPWISE and SLENTRY=0.05 SLSTAY=0.10. What do these two significance level options control?
Select an answer to reveal the explanation.
Short Explanation
Here's the deal — SLENTRY=0.05 sets the p-value cutoff a candidate variable must beat to enter the model, and SLSTAY=0.10 sets the cutoff it must meet to stay. Keeping entry stricter than stay stops variables from cycling in and out.
Full Explanation
In PROC HPREG (and PROC REG) stepwise selection, SLENTRY= specifies the significance level (p-value) that a candidate variable's F-test must achieve to enter the model, and SLSTAY= specifies the significance level that an included variable must maintain to remain in the model. Using SLENTRY < SLSTAY (0.05 < 0.10) ensures the entry criterion is stricter than the stay criterion, preventing cycling. The option describing SLENTRY as a minimum R-squared increase and SLSTAY as a minimum R-squared is wrong because these options are p-value thresholds, not R-squared thresholds. The option saying both are redundant and stepwise uses only the smaller value is wrong because both are distinct and both are used. The option saying SLENTRY limits the number of variables entering to 5% and SLSTAY keeps 10% of all variables is wrong because they are significance levels, not percentage-of-variables limits.