I have calculated the confidence intervals using the reference https://www.lexjansen.com/pharmasug/2003/Posters/P048.pdf and implemented the following code for my dataset;   PROC MEANS DATA=data NOPRINT ; by model reason; VAR default; OUTPUT OUT=xxtmp N=n MEAN=mean STDERR=stderr LCLM=lclm Uclm=uclm ; RUN ; DATA xxtmp15 ; SET xxtmp ; lo = mean - ( TINV ( 0.9 , n-1 ) * stderr ) ; hi = mean + ( TINV ( 0.9 , n-1 ) * stderr ) ; RUN ; However, as a result of it, I receive a lower limit of the confidence interval as negative.   I understand that I should use the log-normal distribution for avoiding the negative lower limit for confidence intervals. However, I don't exactly know how I can give this input to PROC MEANS.   Any help on this is highly appreciated.   Kind regards, Mari       ... View more

Is the following sequence of connecting the Traing/Validation/Test  data sets to Interactive Grouping is right way to do while building the scorecard? SAS Code - Splits the data set into Training and Validation Risk_features_test - is the Test data set   I am wondering for the reason that, since test dataset is also used for interactive grouping, whether this will cause false high performance on the test data set.   Thanks ... View more

I had already prepared the Training and Validation Dataset using the time considerations which needs a specific approach. I had created a separate variable ('TrainingOrValidation') to know whether the observation belongs to a 'Training' or 'Validation' set. Is there any way in SAS E-miner to assign the observation based upon the column values as above. I dont want SAS Miner to split itself as shown here and I am looking for the ways to inform the SAS E-miner about which are the observations belong to Training and which of those remaining belongs to Validation.  I would really appreciate any help.   Thanks ... View more