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.
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
The following email landed in my inbox this morning and it is such a good question that I decided to share because it comes up often. (If you are the sender, thank you for sending this message and pulling me out of a no-blogging-recently slump!) Dear Cat, I need some help
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
Lunch. For some workers, it’s the sweetest part of an otherwise bitter day at the grindstone. Nothing can turn that sweetness sour like going into the breakroom to discover that someone has taken your lunch and eaten it themselves. Nothing like that ever happens here at SAS. But if it
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
Happy New Year!! This is a good time to think about what was going on here in SAS Education one year ago, and to introduce you to a big project that I'm really excited to "take public." In January 2010 (as well as throughout 2009), we kept getting cries for
At this time of year as fans debate over the best players in college football, quantum mechanics combines with college sports to produce the Heisman uncertainty principal: you cannot know who has won the trophy until it is announced, and so you have to treat it as if every candidate
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
