Catherine Truxillo, Ph.D. has written or co-written SAS training courses for advanced statistical methods, including: multivariate statistics, linear and generalized linear mixed models, multilevel models, structural equation models, imputation methods for missing data, statistical process control, design and analysis of experiments, and cluster analysis. She also teaches courses on leadership and communication in data science.
“Dear Cat, I got an email from my IT department that says: [We are nearing capacity on the Flotsam Drive. Please clear data from any folders you are no longer using so we can save disk space. Thanks, The IT Department] Doesn’t this strike you as a bit old-fashioned? I
Outside, the Cary, NC sky is gray and winds are blowing freezing rain, but a group of statisticians at SAS are channeling warm green hills and the soft, gold light of a California evening. Team conversations alternate between distributed processing, PROC IMSTAT and how many pairs of shorts to pack.
“Dear Cat, In a repeated measures drug study, I am unsure what to do with the baseline measurement. Since it is one of the time points in my study, I feel like I should use it as one of the dependent variable measurements. But I have seen analyses where baseline
This morning I had to send my watch off to Switzerland to have its guts replaced. I will be late to everything for the next two weeks. So it was timely that when I got to work, my inbox had the following question from a former student in the Mixed Models
When you think of statistical process control, or SPC for short, what industry first comes to your mind? In the past 10 or 15 years, diverse industries have begun to standardize processes and administrative tasks with statistical process control. While the top two bars of the industrial Pareto chart are
Edited to add: Thanks for Larry Madger for noticing an important omission in my code below. I have updated the programs to include the response variables, which enables the responses to have different means. So, if you were reading last week, we talked about how to structure your data for
Next week's blog entry will build on this one, so I want you to take notes, OK? It's not headline news that in most cases, the best way to handle a repeated measures analysis is with a mixed models approach, especially for Normal reponses (for other distributions in the exponential
A student in my multivariate class last month asked a question about prior probability specifications in discriminant function analysis: What if I don't know what the probabilities are in my population? Is it best to just use the default in PROC DISCRIM? First, a quick refresher of priors in discriminant
Have you used multivariate procedures in SAS and wanted to save out scores? Some procedures, such as FACTOR, CANDISC, CANCORR, PRINCOMP, and others have an OUT= option to save scores to the input data set. However, to score a new data set, or to perform scoring with multivariate procedures that
Last week, a student in my Mixed Models Analysis Using SAS class sent in the following text message during a discussion of crossover designs (sometimes known as ABBA designs, where factors vary within subjects, not ABBA designs where you’re like a Super Trouper). Does it make sense to look at
