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Work & Life at SAS
Page Cvelich 0
True Confessions

True confessions. I’ve been working here at SAS for 15+ years and I have never used the Recreation and Fitness Center. That’s not quite true. I frequently go there to get a diet lemonade or a cup of coffee if our machine is not working. I have picked up some discount movie

Rick Wicklin 0
The contaminated normal distribution

How can you generate data that contains outliers in a simulation study? The contaminated normal distribution is a simple but useful distribution you can use to simulate outliers. The distribution is easy to explain and understand, and it is also easy to implement in SAS. What is a contaminated normal

Data Management
David Loshin 0
Linking identifiers for improved analytics

In my last post we started looking at the issue of identifier proliferation, in which different business applications assigned their own unique identifiers to data representing the same entities. Even master data management (MDM) applications are not immune to this issue, particularly because of the inherent semantics associated with the

Analytics
Paul Gittins 0
The cost of modernization

Unless you live in England, you may not have seen the recent announcement that Buckingham Palace is to undergo a 10-year refurbishment costing the British taxpayer £369M. Even with the post-Brexit devaluation of Sterling, that’s still a sizable spend representing nearly US$500 million. The Queen will remain in residence during

Data Visualization
Sanjay Matange 0
Hotel Text

Yesterday, I published an article on Axis values display, where I mentioned the desire expressed by many users to get x-axis tick values in Hotel text orienttion.  The name comes from the way many hotel signs are displayed as shown on the right.  Such arrangement of text can also be very

Advanced Analytics
Imre Pólik 0
How good is the MILP solver in SAS/OR?

There has been a lot of speculation over the years about the quality of the optimization solvers in SAS/OR, in particular the mixed integer linear optimization (MILP) solver. Measuring the performance of optimization solvers and comparing different solvers on a test set is a crucial part of modern optimization solver development.

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