Using The SAS VS Code Extension to Check Prime Distribution
Recent Library Articles
Recently in the SAS Community Library: @RyanKing showcases features in the SAS Visual Studio Code extension, including data exploration using SAS, SQL and Python in one SAS notebook file.
Boa tarde, tenho duas tabelas e gostaria de gerar/exportar um arquivo único, em que, cada tabela ficasse em uma aba do excel. Como fazer este procedimento?
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Hey SAS Community, I am new to SAS and would appreciate any advice on this topic. For a university project, I need to calculate the expected sales value for the upcoming months after my dataset runs out. The dataset includes Total_amt , which contains the transaction values, and Tran_date , which specifies the dates of the transactions. data TransactionsWithSasDate;
set Transactions;
Tran_date = mdy(Month, Day, Year);
format Tran_date date9.;
run;
proc sql;
create table MonthlySales as
select
intnx('Month', Tran_date, 0, 'Beginning') as Month format=date9.,
sum(Total_amt) as MonthlySalesValue
from TransactionsWithSasDate
group by calculated Month;
quit;
proc arima data=MonthlySales;
identify var=MonthlySalesValue(12);
estimate p=1 q=1;
forecast lead=12 id=Month interval=Month out=ForecastedSalesValue;
run;
proc sgplot data=ForecastedSalesValue;
series x=Month y=MonthlySalesValue / lineattrs=(color=blue) legendlabel="Actual";
series x=Month y=Forecast / lineattrs=(color=red) legendlabel="Forecast";
xaxis label='Month';
yaxis label='Monthly sales value';
title 'Monthly Sales Trend and Forecast';
run; I double-checked my code, but I am not sure if it is correct because the output graph looks a little off. Any advice on this topic would be highly appreciated! Greetings, Johannes
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Hi everyone, data tab1;
input gr $ experiment var1;
datalines;
A 1 0.58
A 2 0.74
A 3 1.17
B 1 0.73
B 2 0.75
B 3 1.52
C 1 1.09
C 2 1.06
C 3 1.60
;
run;
proc npar1way data=tab1 wilcoxon dscf;
class gr;
var var1;
run; I have 3 subjects (A, B and C). For each subject, i repeat an experient 3 times. So I have 3 quantitave measurements for each subject. Looking at the data, I realize that in each experiment (1, 2 and 3), A < B < C. But for each experiment I don't have the same "order of magnitude" for my quantitative variable, because of manipulation errors. I wanted to do a kruskall-wallis because of the small sample, to test my quantitative variable between my 3 groups. But I don't know how to take into account the fact that there is inter-experimental variability because one and only one CLASS variable must be specified ? And is the kruslll-wallis test the best solution ? Thank you.
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I'm trying to use MIANALYZE on the ODS output dataset called ROCASSOCIATION that comes out of the logistic procedure. I'm trying to combine multiple AUCs. Help please!
ods output ROCAssociation=ROCAssociation;
proc logistic data=both plots=roc(id=prob);
by _imputation_;
model y(event='Yes') = x / nofit ;
roc 'X' x;
run; Then... PROC MIANALYZE ??????
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I'd generate the cumulative incidence rate at several specific time points by using proc lifetest. Code was shown below. However, I still get the cumulative incident rate at continuous time points. Could you help me figure it out?
proc lifetest data=data intervals=0 to 12 by 1 method = lt;
time Month*death(0)/EVENTCODE=1;
strata group;
run;
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