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Fixing character data without beatings: SAS Enterprise Guide

At the JMP seminar on Monday, when Dick De Veaux said that 65-70% of time in all research projects is spent on data cleaning, everyone in the audience groaned in agreement. One of the biggest problems I run into is recoding those simple textboxes. For example, we often want to look at data for one…

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When acceptance is really rejection: Death by Green Pants

The model is non-significant, therefore my theory is supported. Huh? Just when you thought it was safe to get back into statistics… It took you two years of graduate school but now you have it down. P-value low = good, relationship detected, publication, tenure, Abercrombie & Fitch models at your feet. P-value = high, no…

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Controlling for Damn Near Everything: Propensity Score Matching

Lately I have been on a roll looking at relatively less common statistical techniques, proportional hazards, survival analysis, etc. In keeping with that, I have been taking a look at propensity score matching, fondly known as PSM by, – well, by no one actually. The problem to be solved …. Think about some of these…

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Get out of your bubble

Whether you are a statistician, SPSS guru, SAS programmer or professor and world-renowned expert on re-incarceration, odds are great that you are susceptible to bubble-vision. You work, breathe and socialize within one or two very narrow bubbles. This is bad and unhealthy. You’ll miss much of life that is beautiful, exciting, dramatic, interesting, tragic and…

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My Adventures with SAS 9.2 v2 and sexual harassment

FINALLY got a few minutes to download the latest version. For some reason the download I received was for the planned installation as opposed to the basic installation. In 25 words or less, basic installation is for stand-alone installs on a single machine, which we have hundreds of users doing. The planned installation would be…

Random non-parametric thingies: This is your brain on stats

Here is how the Wald statistic works: You divide the maximum likelihood coefficient estimate by its standard error and square the result. If you wanted to be really specific about it, what you are dividing is the difference between the obtained coefficient estimate and your hypothesized estimate. I would say, though, that 99% of the…