Many of you have heard of R (the R statistics language and environment for scientific and statistical computing and graphics). Perhaps you know that it uses command line input rather than pull-down menus. Perhaps you feel that this makes R hard to use and somewhat intimidating!
OK. Indeed, R has a longer learning curve than other systems, but don’t let that put you off! Once you master the syntax, you have control of an immensely powerful statistical tool.
Actually, much of the syntax is not all that difficult. Don’t believe me? To prove it, let’s look at some syntax for providing summary statistics on a continuous variable. (more…)
You have probably noticed I’m not much into R (though I’m slowly coming around to it). It goes back to when I was in my graduate statistics program, where we were required to use SPlus (R’s parent language—as far as I can tell, it’s the same thing, but with customer support).
We were given a half hour tutorial and an incomprehensible text, and sent off to figure it out how to use SPlus on graduate level stats.
Not fun.
And since I was already fluent in SAS, SPSS, and BMDP (may it rest in peace), I resisted SPlus. A lot.
I actually wish R had been around, (more…)
If you are a SPSS, SAS, or Stata user who finds yourself needing to use R (I mean, it’s free), I just found this great website: http://statmethods.net/index.html.
In addition to the five listed in this title, there are quite a few other options, so how do you choose which statistical software to use?
The default is to use whatever software they used in your statistics class–at least you know the basics.
And this might turn out pretty well, but chances are it will fail you at some point. Many times the stat package used in a class is chosen for its shallow learning curve, (more…)
