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.
Debido a la complejidad y cambios en el mercado, las organizaciones de todo el mundo están aprovechando las oportunidades para hacer mejores predicciones, identificar soluciones y dar pasos estratégicos y proactivos, lo que significa que dependen cada vez más de los big data. Sin embargo, en su búsqueda de resistencia
With so much complexity and change in the marketplace, organizations worldwide are leveraging opportunities to make better predictions, identify solutions and take strategic, proactive steps forward – which means that they increasingly depend on big data. In their quest for organizational resilience, however, companies find that numbers aren’t necessarily the secret
“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
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
