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 just any model. It’s a three-level multilevel generalized linear mixed model with a binary response. You’ve used GENMOD before. You’ve used MIXED before. Now the two procedures have been sitting in a tree, K-I-S-S-I-N-G, and along comes GLIMMIX in a baby carriage.

We’ve all been there.

Here are some tips for first-time users of PROC GLIMMIX.

Continue reading "Mixed Feelings about Logistic Regression: Eight Hints for Getting Started with PROC GLIMMIX"

Today I took my 3-year old, Elizabeth, to lunch at the on-site cafeteria. As she enjoyed some mac & cheese, we noticed a spot on the floor.

Elizabeth: What’s that, mommy?
Me: I don’t know, I think it must be where the rug got messed up and they covered it with tiles. Or maybe that’s an access point to something electrical. Or maybe it’s for decoration.
E: No, mommy, I think that when it’s nighttime, and the people are gone, that’s where the possums have their weddings. And when there are no possum weddings, the mice use that spot to make Cinderella’s dress for the ball.

How can this bit of toddlerish wisdom make you a better data analyst? Sometimes you find anomalies in the data. Things that you might not give a second thought to. Outliers, unusual combinations of variables, maybe a funky-looking standard error (how is it that big?). You might brush it off as something mundane.

Continue reading "Possum Romance and Other Data Analysis Anomalies"

Currently I'm ...

Recently finished Analytics at Work: Smarter Decisions, Better Results by Davenport, Harris, and Morison (2010, Harvard Business Press). I've assigned it as companion reading for the students who take the Advanced Business Analytics course I'm writing right now. It is an excellent reference for anyone who wants a roadmap for how to help their organization embrace analytics. The book stops short of explaining statistical analyses, leaving those topics to other writers, but what most good data mining books lack, this book addresses the characteristics of analytical leadership, pitfalls to implementing analytics in a business, and ways to get management buy-in for analytics.

Writing
Advanced Business Analytics 1. This course is a collborative effort by statisticians in the Education division at SAS and professors at some of the top business schools in the US. The course will be given, free of charge, to professors at graduate programs in universities around the world, in an effort to sharpen the analytical teeth of tomorrow's business leaders. The course is largely designed for MBA students, although we are hearing buss from statstics, economics, marketing, and management information systems departments as well. We are preparing a kick-off training for about 35 professors this summer, and will likely hold additional training event in the future, should demand warrant. The course is essentially an introduction to data mining with a heavy emphasis on solving business problems. Students coming out of the course should be prepared to manage analytical staff, to make smart decisions when working with statistical analysts, and move their businesses toward a more analytical model of decision making.

Listening to
I have been on a really weird Gilbert and Sullivan kick lately. Why? I used to listen to all of their operettas as a kid, and now I find myself trying to sing something from Yeomen of the Guard or the Pirates of Penzance to my kids and I have to download the song to get the lyrics right. Well, at least it has cleared the ABBA backlog I had in my brain the last 3 months. (Thank you very much, Mamma Mia DVD, for making my 3-year-old a diva).

Hearing
I'm hearing a lot more about structural equation modeling lately. It is no coincidence that JMP and SAS/STAT development teams are producing a point-and-click interface to perform SEM within JMP, which calls the newly-improved PROC CALIS. It's a really slick interface, if you get a chance to preview it at a conference. The sudden resurgence of interest in SEM is encouraging, because SEM is one of the more interesting modeling techniques around. I'm hearing a lot about SEM in pharmaceuticals, manufacturing, and insurance. If you have an example where your organization is using SEM, shoot me a comment or an email to share how you're using it. I'd love to hear from you!

Considering
John Naisbitt's High Tech High Touch. Chatting with a colleague, Tom Henry, in the breakroom this morning got us thinking about how important it is to have personal contact with customers. Not everyone wants or needs to talk to a sales rep (I am generally in the category of "Don't call me, I'll call you" when it comes to sales reps). With the great advances in data integration, automated analytics, scoring, and marketing automation, there are fewer reasons than ever to have to talk to a real person when purchasing a product or service. But then there are situations that scream for an exception. A customer calls software sales, wanting a training quote for an Enterprise Guide course, covering programming to address stacking and nesting, preparing data for perceptual mapping. No automated system will place this person into the right course. Tom recognizes that this is the time to pick up the phone and talk -- what do you need to do? What are you doing now? What do you need to do differently? At the end of the day, high tech is able to clear the space for Tom to have that critical High Touch with someone who really needed it. And he won't waste time calling someone who would rather just "buy online."

Do you have technlogy working for you to make it easier to identify those critical touch situations?

The SAS Training Post is born, and introductions are in order. I'm Catherine Truxillo, but everyone except my mom calls me Cat. I have been a statistician in the Education division at SAS since 2000. I've been blogging a little longer than that, although this will be the first I've ever mentioned of work.

My posts will largely deal with statistical analysis using SAS, although I plan to cast a wide net to business applications of analytics, research methodology, puzzles, and an insider's view of life in building H in Cary, where most of the SAS Statistical Training department are based. And let's quell the rumors right now: yes, we wear shoes. Almost every single day.

You are welcome to lurk, but I prefer you to drop me a note. I welcome your comments and look forward to connecting you with statistical training at SAS.