Hi @chelsealutz     After finding the potential factor/variable  for inclusion in the model using any of: - selection = stepwise slentry = 0.15 slstay = 0.15; - selection = forward  slentry =0.15 - selection = backward slstay = 0.15 - selection = score , for both quantitative and categorical variables and interaction term - you can compare models based on following criteria:    -2LogL The value itself is not important. It is used to compare two nested models, model with smaller -2LogL is better. Difference in -2LogL between two nested models is approximately distributed as Chi-square. AIC (Akaike Information Criterion) AIC is used to compare non-nested models on the same sample. AIC value itself is not meaningful but the model with the smallest AIC is considered the best. SC (Schwarz Criterion) Model with smallest SC is most desirable but the value itself is not meaningful. Like AIC, it is appropriate for non-nested models. ROC Area The area under the ROC curve is a measure of the model’s ability to discriminate between event and non-event: Large values are desirable (predictive accuracy for (event, non-event) pairs). ROC = 0.5: no discrimination (no better than coin toss) 0.7 <= ROC < 0.8: acceptable discrimination 0.8 <= ROC < 0.9: excellent discrimination ROC > 0.9: outstanding discrimination Brier’s Score Small values are desirable.        ... View more

To my knowledge nobody ever reported a problem with the intck function. Try the following:   data _null_; do Year = 1995 to 2016; date28feb = mdy(2,28,year); date01mar = mdy(3,1,year); nbDays = intck("DAY", date28feb, date01mar); put (_all_) (=); end; format date: yymmdd10.; run; ... View more

In addition is often use proc freq to:   Check skip patterns of responses in survey data or any data entry system. Example: Responses to question 10 should match numbers or response categories 3 and 4 to Question 6. Table q10*Q6; will show me right away if there is a data issue.   Proc freq is almost always the first tool I look to when doing any kind of data cleaning or just getting to know new data set. It may provide voluminous data if there are lots of continuous variables but the syntax simplicity can't be beat: Proc freq data=mydata; run; I know I'm going to get lots, but I get to see values, labels if assigned, how many missing values for each variable (VERY helpful if there shouldn't be any for one or more variables). And sometimes just plain data entry stupidity ( or a system induced behavior that assigned many supposedly poor clients with monthly incomes of 83333).   Verify that I have all values of a variable considered when designing a custom format. ... View more