Solving the analyst’s toughest problems with SAS

If you’re an analyst, you know discovery in a complicated data set is one of the toughest problems to solve. But did you know the Business Knowledge Series course, Exploratory Analysis for Large and Complex Problems Using SAS Enterprise Miner, can help you solve those issues by tackling real-world problems? […]

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SAS graphs, everywhere along your business pipeline!

"It's a floor wax, and a dessert topping" - this pretty much describes SAS/Graph! (bonus points if you know where this quote came from!) Some people think of SAS as just a quality control tool. Others think of it as just a sales & marketing tool. And yet others think […]

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Five myths about unstructured data and five good reasons you should be analyzing it

“How can we begin to make sense of the unstructured data, when we still don’t make the most of our structured data?” said the exasperated senior manager from a large retail firm. One of the great pleasures of my job is the relationship with students that continues after class has […]

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Basketball tournaments, Moneyball, and sports analytics

A big part of  "winning" these days (be it sports or a business) is performing analytics better than your competition.  This is demonstrated in awe-inspiring fashion in the book (and movie) "Moneyball."  And on that topic, I'd like to show you a few ways SAS can be used to analyze sports data […]

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The Human Side of Statistical Process Control: Three Applications of SAS/QC You Might Not Have Thought About

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 […]

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Who Ate My Lunch? Discriminant Thresholds to Reduce False Accusations

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 […]

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The Punchline: MANOVA or a Mixed Model?

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 […]

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Data Structure for Repeated Measures Analysis... A Teaser

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 […]

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Discriminant Analysis, Priors, and Fairy-Selection

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 […]

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We Wanted to Call It "All the Statistics You Missed in B-School"

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 […]

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