Adding to the suggestions by @ballardw here is a third method   Data want; set have; if age > <limit> then age=limit; run; And a fourth method:   Do nothing, it's not an outlier. An age of 40 seems to me to be not an outlier, but we don't know how you collected the data, and what population you collected the data from.   So, @gtucke1 to quote from @ballardw, "Which to choose depends on your needs/wants which have not been very clearly stated." YOU have to decide what to do about a given outlier, YOU have to state your needs. We can write the code, but we can't tell you what the right decision is. ... View more

The discussion on this thread inspired me to think about this problem and write up a solution. My approach: Use base 26 to convert a set of unique integers into a set of unique strings. If you expect to assign IDs to N subjects, you can use strings that have k characters, where N < 26^k.   How to generate the ID values (strings) from integers: "Base 26: A mapping from integers to strings" How to randomly select N unique IDs: "Generate random ID values for subjects in SAS" The primary advantage of this technique over some of the other proposals is that it ensures uniqueness of the ID values. You don't have to check whether a random string has already been assigned. ... View more

@Quentin - @PGStats 's version is reasonably acceptable in my view, the OPs's not so much 🙂 ... ... View more

This reminds me of one of the first pieces of advice I got from the American mentors in my second career as an  American Football game official: "If you don't want to do it right, then why are you doing it at all?" ... View more

@yaakov555 wrote: 1,2,3,4,5 Those are the numbers that the counter produces. It does not explain what is being counted. ... View more

data have; input col1 col2 $; datalines; 1 A 2 B 3 C ; data want(keep = col3); do _N_ = 1 by 1 until (z); set have end = z; length s $200; s = cats(s, col2); end; do _N_ = 1 to _N_; set have; col3 = substr(s, _N_); output; end; run; ... View more

  @FreelanceReinh wrote: It's true that the sequences of random numbers differ between RandSQL1 and RandSQL2 in this example, but the difference is still "systematic" as the order is just reversed: If the second ORDER BY clause is changed to order by cnt desc; the differences regarding variables U and N vanish (at least on my computer) even when MYDATA1 was extended from 10 to 10 million observations.   When the join order is changed and the sort order is changed, the original random sequence is recovered, but the random values are no longer associated with the same cnt values.   I get exactly the same results as you describe when I run your many-to-many join example on a SODA (SAS On Demand for Academics) server.   To anybody considering to call the SETINIT function in proc SQL I say DON'T ! ... View more

ANOVA compares observarions, not variables. So you need to transpose your data first. Something like:   data have; input Student $ Grad y2016 y2017 y2018 y2019 y2020; datalines; student1 2018 58 83 108 . . student2 2018 60 86 103 . . student3 2018 63 80 110 . . studenta 2018 59 82 106 . . student4 2019 . 57 84 113 . student5 2019 . 61 79 117 . student6 2019 . 64 82 109 . studentb 2019 . 60 81 115 . student7 2020 . . 55 80 117 student8 2020 . . 61 82 121 student9 2020 . . 62 87 116 studentc 2020 . . 57 85 118 ; proc transpose data=have out=temp1 prefix=yr; by student grad notsorted; run; data temp2; set temp1; if yr1; year = input(substr(_name_,2), 4.); aYear = year - grad + 3; /* student academic year */ keep student grad year aYear yr1; rename yr1 = labHours; run; proc glm data=temp2; class aYear year; model labHours = aYear year / solution; lsmeans aYear; lsmeans year; run; Note: this is testing for main effects only. Interactions between year and academic year are not estimable. ... View more

Hi @PGStats , Thank you so much. Wilcoxon was exactly what I was looking for. And also, thank you suggesting my second question had to do with interval-censored data. I think this is exactly what my professor meant. MJ ... View more

data _null_; x='01jan2005'd; d1=intnx('qtr',x,1,'s')-1; d2=intnx('qtr',x,2,'s')-1; d3=intnx('qtr',x,3,'s')-1; put (x d:) (= date9.); run; ... View more

With SQL you could do:   proc sql; select * from mydata group by id having abs(days) = min(abs(days)); quit; ... View more

if i understand your code correctly, this might be a bit cleaner for the query optimizer.   PROC SQL; CREATE TABLE products AS SELECT C.PRODCD ,C.PRODTYP ,C.PRODMAN ,S.SALESAREA ,S.MKTNBR FROM PRODTBL C INNER JOIN ( SELECT s.ProdCD FROM SALESTBL a INNER JOIN MKTLISTING b on a.MKTNBR = b.MKTNBR ) s ON C.PRODCD = S.PROD CD ; QUIT; ... View more

The function call removes leading zeros if starting at the first character. Is equivalent to prxchange("s/^0+//", 1, NBR). Note that prxchange("s/^\s*0+//", 1, NBR) would account for leading spaces. ... View more

I didn't realize how big this could grow. Like I mentioned I do need to take into account other variables as the size and cost which would limit the amount of possible combinations allowed. I've been searching through the forum and I think my question has a lot of similarities with the knapsack problem. Found this post that might be useful: https://communities.sas.com/t5/SAS-IML-Software-and-Matrix/Variation-of-Knapsack-Problem/td-p/569457 ... View more