The DO Loop
Statistical programming in SAS with an emphasis on SAS/IML programs
Many modern statistical techniques incorporate randomness: simulation, bootstrapping, random forests, and so forth. To use the technique, you need to specify a seed value, which determines pseudorandom numbers that are used in the algorithm. Consequently, the seed value also determines the results of the algorithm. In theory, if you know
I have previously blogged about ways to perform balanced bootstrap resampling in SAS. I recently learned about an easier way: Since SAS/STAT 14.2 (SAS 9.4M4), the SURVEYSELECT procedure has supported balanced bootstrap sampling. This article reviews balanced bootstrap sampling and shows how to use the METHOD=BALBOOT option in PROC SURVEYSELECT
In categorical data analysis, it is common to analyze tables of counts. For example, a researcher might gather data for 18 boys and 12 girls who apply for a summer enrichment program. The researcher might be interested in whether the proportion of boys that are admitted is different from the