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Learn SAS
Maggie Miller 0
25 Fun Facts about SAS Press authors

You’ve read their books. You’ve probably even met them at conferences. But now, we’re revealing another side of our beloved SAS authors in this list of fun facts. Prepare to be surprised. Hint – one of the authors swims with sharks. Tricia Aanderud Tricia has over 100 jokes memorized -

Donna McGuckin 0
How size optimization helps retailers improve inventory productivity and profitability

All of us in the retail and wholesale industry, regardless of role, are responsible for the same objectives: Increase customer satisfaction, inventory productivity, and profitability by way of localization and omni strategies. We've learned that our best strategies sometimes fall short; we spend significant effort analyzing, only to achieve marginal results. Many of

Data Visualization
Sanjay Matange 0
Axis Customizations

All axis customization features are always welcome.  Especially since SGPLOT statements can often be used to create non standard graphs, having the ability to customize the axes is important.  This article presents ways in which you can customize the discrete axes. By default, the x axis will try to display the

Data Management
Dylan Jones 0
Has Big Data had its day?

No one knows for sure who coined the term Big Data. Despite etymological studies, we are still no closer to attributing provenance to any one person, or indeed any one period. Some say the term was coined in the '80s, others believe the '90s – and many are convinced the term originated

Data Management
Caroline Hermon 0
Sizing: the long and short of it

Sizing is a topic that solutions managers typically leave until the end after decisions about the application have been settled. But there are often many variables that can impact the final size requirement. We have seen across our customer base that sizing and the number of environments has been determined

Rick Wicklin 0
Models and simulation for 2x2 contingency tables

When modeling and simulating data, it is important to be able to articulate the real-life statistical process that generates the data. Suppose a friend says to you, "I want to simulate two random correlated variables, X and Y." Usually this means that he wants data generated from a multivariate distribution,

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