About stat_sas

stat_sas · ‎08-12-2016

Data set "have" would be based on information provided. I've added syntax to create data set. Please try this: data have; input ResolvedTeam $ ProductResposibleTeam $; datalines; A X A Z B P C O A O C P D W ; data want; set have; NewColumn=ResolvedTeam; if ResolvedTeam='A' then NewColumn=ProductResposibleTeam; run;

stat_sas · ‎08-12-2016

Hi, Please try this: data want; set have; NewColumn=ResolvedTeam; if ResolvedTeam='A' then NewColumn=ProductResposibleTeam; run;

stat_sas · ‎08-09-2016

Hi, Why you need to merge. This can be done in this way: data File1; input ID; datalines; 1055 1058 1059 ; data want; set File1; do group=1 to 6; output; end; run;

stat_sas · ‎08-09-2016

Hi, Proc sql will generate the desired output: proc sql; select ID, A1,count(*) as count from have group by ID,A1; quit;

stat_sas · ‎08-09-2016

Hi, proc surveyselect data=stores1 out=devsample method=srs samprate=0.5 outall; run; outall option creates a variable "selected" that provides a flag to separate two data sets. You need a data step to create two data sets. Please try this after proc surveyselect: data develop validate; set devsample; if selected then output develop; else output validate; run;

stat_sas · ‎08-09-2016

Hi, If a variable is normaly distributed then we expect 68% of the observations fall within mean+/-1.standard deviation 95% of the observations fall within mean+/-2.standard deviation 99% of the observations fall within mean+/-3.standard deviation

stat_sas · ‎07-18-2016

Hi, Please see below a sql solution: proc sql; create table want as select a.*, cdate from have1 a left join have2 b on cdate<date group by date having (date-cdate)=min(date-cdate) order by date; quit;

stat_sas · ‎07-18-2016

Hi, Please try this. You need to create id variable to bring back data in its original form. As per desired output you need to sort it by phase variable that will bring data in a sequence (0s and 1s together) and from there iterate time variable. Once this is done you can bring back data in original shape by sorting it id variable. data have; input Study Case Phase; id=_n_; datalines; 1 1 0 1 1 0 1 1 1 1 1 1 1 1 0 1 1 0 1 1 1 1 1 1 1 2 0 1 2 0 1 2 1 1 2 1 1 2 0 1 2 0 1 2 1 1 2 1 2 1 0 2 1 0 2 1 1 2 1 1 3 1 0 3 1 0 3 1 1 3 1 1 3 1 0 3 1 0 3 1 1 3 1 1 3 2 0 3 2 0 3 2 1 3 2 1 3 2 0 3 2 0 3 2 1 3 2 1 ; proc sort data=have; by Study Case Phase; run; data want; set have; by Study Case Phase; if first.study or first.case or first.Phase then Time=0; Time+1; run; proc sort data=want out=want(drop=id); by id; run;

stat_sas · ‎07-16-2016

Hi, Please provide some sample data. Also, expain your question a bit more.

stat_sas · ‎07-16-2016

Hi, Please try this. data want; set have; substr(strip(CONT_CD),1,1)=''; run;

stat_sas · ‎07-15-2016

Hi, It depends, if you are happy with 3 cluster solutions with 7 factors then you can drop 8th factor.

stat_sas · ‎07-15-2016

Hi, If variables are highly correlated then first step after running factor analysis is to see eigen values which represent variation explained by factors. As an example, if first eigen value explains 80% of the variation then first factor score would be sufficient for subsequent analysis and this can also be used as a representative of four variables. On the other hand, if more top two eigen values significantly expains most of the variation in orginal variables then you have to use two factor scores in regression analysis. Lastly, running separate factor analysis may produce factor scores which may be again correlated and can introduce overfitting and destablize parameter estimates.

stat_sas · ‎07-15-2016

Hi, What do you mean by "how to combine 3 continuous and 1 binary variable and label the resulting factor"? Are hhincome ageofmom banked highest uncorrelated?

stat_sas · ‎07-15-2016

Hi, Why are you running two separate factor analysis? Can't it be done in a single run?

stat_sas · ‎07-15-2016

Hi, Please try this. data want; set have; Var1=coalesce(var1,var2,var3,var4); run;

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