Here is one way: options datestyle=dmy; data have;   informat Start_date $30.;   input Start_date &;   cards; 11/1/2009 0:00:00 4/2/2009 0:00:00 26/1/2009 0:00:00 26/11/2009 0:00:00 26/11/2009 12:00:00 AM 1/11/2009 0:00:00 1/11/2009 12:00:00 AM ; data want (drop Start_date_in);   set have (rename=(Start_date=Start_date_in));   format Start_date mmddyy10.;   Start_date=input(scan(Start_date_in,1,' ','r'), ddmmyy10.); run; I would have thought one could do it by inputting the entire datetime string, and using the anydtdtm informat, but it didn't work correctly for the 3rd from the last record. ... View more

I'd go farther than LVM and say that significance/nonsignificance isn't really a good reason for including or dropping ANY variable, although it is one that is often used. I've had bosses/clients insist on it. Effect size is more important. With a large N, as has been said, it's easy to get significance. And, as also been said, the intercept is usually not of interest, but that is a reason for leaving it IN, not taking it OUT. With 5,000 cases and 15 IVs, unless the DV is very unevenly distributed, you have plenty of cases per variable. Why not include ALL the IVs? How did you choose them in the first place? Are they substantively important? ... View more

Thank you :-), now i have created dummy variables for the class variables, i will use them in clustering without standardizing. however i have some outliers in 2 variables (1 continuous variable and 1 discrete variable), so do i need to do the outlier treatment first or stadadrization first. ... View more