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Learn SAS
Kathy Council 0
More eBooks for everyone

SAS Publishing has been offering eBooks through partners like Amazon, Apple, and Google, for a number of years. Our content is also available through subscription-based companies like Books 24x7, Safari, and EBSCO. We have learned that taking content developed for hardcopy and turning it into an ebook is not a

Rick Wicklin 0
Inverse hyperbolic functions in SAS

I was recently asked, "Does SAS support computing inverse hyperbolic trigonometric functions?" I was pretty sure that I had used the inverse hyperbolic trig functions in SAS, so I was surprised when I read the next sentence: "I ask because I saw a Usage Note that says these functions are

Data Management
Annette Marett 0
SAS loves math: Wendy McHenry

From figuring out the optimal price a company should charge for soup to forecasting an organization's financial outcomes, each day brings a new business challenge for Wendy McHenry, systems engineer at SAS. Her "How can we help?" attitude is only part of the equation for successful customer relationships. Find out how math

Mike Gilliland 0
Simple methods and ensemble forecasting of elections

Two enduring principles of forecasting are that simple methods can work as well as fancy methods, and that combining (averaging)  forecasts, also known as "ensemble forecasting," will usually result in more accurate predictions than the individual methods being averaged. We saw a good demonstration of these principles in Tuesday's election

Learn SAS
Shelly Goodin 0
SAS author's tip: The basics of decision trees

This week's SAS tip is from Barry de Ville and his book Decision Trees for Business Intelligence and Data Mining: Using SAS Enterprise Miner.  Barry is a technical and analytical consultant at SAS. To learn more about Barry and his forthcoming new edition of the book, following this week's excerpt, visit his author

Shelley Sessoms 0
A snapshot of SCSUG 2012

I’ve just returned from my third regional conference this year…SCSUG in Houston. We had great attendance; 207 folks registered for the event! And several of our authors were there: Kirk Lafler, Sanjay Matange and Cynthia Zender. SAS’ very own Paul Kent gave the keynote address on big data and high

Mike Gilliland 0
The predictive power of nonsense

The 2012 US Presidential race comes to a close today (thankfully), and there is no shortage of wacky indicators predicting the winner: Iowa Electronic Markets FiveThirtyEight PollyVote University of Colorado In primitive times a diviner could foretell the future by poisoning a chicken -- whether it lived or died provided

Rick Wicklin 0
Constructing common covariance structures

I recently encountered a SUGI30 paper by Chuck Kincaid entitled "Guidelines for Selecting the Covariance Structure in Mixed Model Analysis." I think Kincaid does a good job of describing some common covariance structures that are used in mixed models. One of the many uses for SAS/IML is as a language

Analytics
Annette Marett 0
SAS loves math: Udo Sglavo

“Maybe math is not love-at-first-sight for you, but it pays to flirt with it a little,” says Udo Sglavo.  As a principal analytical consultant in the operations research R&D group at SAS, he’s not your typical mathematician. Udo arrived at his career with a gentle tug from family members and

Analytics
Courtney Peters 0
3 ways to improve customer loyalty with analytics

It’s no secret that analytics helps large organizations determine what offers are best for their customers and their business, but can powerful analytics be harnessed for small and medium business success? For the answer, I turned to Oberweis Dairy, a 90-year-old mid-sized business that has grown from a family-owned dairy farm

Advanced Analytics
Rick Wicklin 0
Compute the log-determinant of a matrix

The determinant of a matrix arises in many statistical computations, such as in estimating parameters that fit a distribution to multivariate data. For example, if you are using a log-likelihood function to fit a multivariate normal distribution, the formula for the log-likelihood involves the expression log(det(Σ)), where Σ is the

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