This quick note serves as a supplementnote of my previous Statistical Notes (3): Confidence Intervals for Binomial Proportion Using SAS which I will extend as a SESUG 2015 paper. Basically I added a new Blaker method to my CI_Single_Proportion.sas file and found more CIs from SAS PROC FREQ. First of all, call the script: filename […]
Tag: Statistics
R Now Contains 150 Times as Many Commands as SAS
by Bob Muenchen In my ongoing quest to analyze the world of analytics, I’ve updated the Growth in Capability section of The Popularity of Data Analysis Software. To save you the trouble of foraging through that tome, I’ve pasted it below. … Continue reading →
Calculating Covariance by SAS, A Brutal Way
It was very disappointed that there is only one built-in method to calculate covariance in Base SAS: that’s in PROC CORR (while you can also do it in SAS/IML, of course): The following is a quick-and-dirty way to get a function like %COV: %macro COV(data, var1,var2); %local _cov; %let rc = %sysfunc(dosubl(%str( ods output […]
Stata’s Academic Growth Nearly as Fast as R’s
by Bob Muenchen Analytics tools take significant effort to master, so once learned people tend to stick with them for much of their careers. This makes the tools used in academia of particular interest in the study of future trends … Continue reading →
Confidence Intervals for Binomial Proportion (Again): A Quick Note
In Lex’s library of the latest SAS Global Forum 2015 papers, I found an interesting paper by Wu Gong, Jeffreys Interval for One-Sample Proportion with SAS/STAT Software, where SAS MCMC procedure and a so called Random Walk Metropolis Algorithm were implemented to calculate the Jeffreys interval for binomial proportion. Years ago I wrote several posts […]
Fastest Growing Software for Scholarly Analytics: Python, R, KNIME…
In my ongoing quest to “analyze the world of analytics”, I’ve added the following section below to The Popularity of Data Analysis Software: It would be useful to have growth trend graphs for each of the analytics packages I track, … Continue reading →
Google Scholar Finds Far More SPSS Articles; Analytics Forecast Updated
Only last August I wrote that among scholars, the use of R had probably exceeded that of SPSS to become their most widely used software for analytics. That forecast was based on Google Scholar searches focused on one year at a … Continue reading →
It’s Analytics Survey Time!
Every other year Rexer Analytics surveys Data Analysts, Predictive Modelers, Data Scientists, Data Miners, and all other types of analytic professionals, students, and academics regarding the software they use. I then update the main results in The Popularity of Data Analysis … Continue reading →