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
Editor's Note: The following question was recently asked of our statistical training instructors. Terry Woodfield, along with Bob Lucas took the time to write this eloquent and easily digestible answer. Question: I'm trying to get a general – very general – understanding what the Bayes theorem is, and is used
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
Delicious Mixed Model Goodness Imagine the scene: You’re in your favorite coffee shop, laptop and chai. The last of the data from a four-year study are validated and ready for analysis. You’ve explored the plots, preliminary results are promising, and now it is time to fit the model. It’s not
