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Programming Tips
Susan Slaughter 0
What's wrong with this code?

Whether you enjoy debugging or hate it, for programmers, debugging is a fact of life. It’s easy to misspell a keyword, scramble your array subscripts, or (heaven forbid!) forget a semicolon. That’s why we include a chapter on debugging in The Little SAS® Book and its companion book, Exercises and

Advanced Analytics | Analytics | Artificial Intelligence | Machine Learning
Charlie Chase 0
Rapid demand response forecasting helps retailers adapt during COVID-19

Rapid demand response forecasting techniques are forecasting processes that can incorporate key information quickly enough to act upon in real time by agile supply chains.   Retailers and consumer goods suppliers are urgently trying to determine how changes in consumer behavior will affect their regions, channels, categories, brands and products during

Work & Life at SAS
Lisa Allred 0
PRIDE Everywhere

  June is PRIDE month. Even before I joined the leadership team for the SAS PRIDE Employee Inclusion Group, I always used my Work/Life blog in June to write about LGBTQ youth and how we can support them. This year I struggle to find words that are adequate to educate,

Alyssa Grube 0
Doing good and giving back

Written by Allison Hines and Kara Roberts  Over the last few weeks, we've shared stories of how SAS employees are banding together while staying apart during the COVID-19 crisis. Our people have donated their time and skills to the cause, including making masks for healthcare workers, leading virtual dance classes for students with special needs and hosting trivia games for charity via Zoom – among many other efforts. Keep reading

Advanced Analytics | Machine Learning
Austin Cook 0
Monotonic Constraints with SAS

A monotonic relationship exists when a model’s output increases or stays constant in step with an increase in your model’s inputs. Relationships can be monotonically increasing or decreasing with the distinction based on which direction the input and output travel. A common example is in credit risk where you would expect someone’s risk score to increase with the amount of debt they have relative to their income.

Advanced Analytics | Machine Learning
Rick Wicklin 0
The Kullback–Leibler divergence between continuous probability distributions

In a previous article, I discussed the definition of the Kullback-Leibler (K-L) divergence between two discrete probability distributions. For completeness, this article shows how to compute the Kullback-Leibler divergence between two continuous distributions. When f and g are discrete distributions, the K-L divergence is the sum of f(x)*log(f(x)/g(x)) over all

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