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Rick Wicklin 0
Random number seeds: Only the first seed matters!

The other day I encountered the following SAS DATA step for generating three normally distributed variables. Study it, and see if you can discover what is unnecessary (and misleading!) about this program: data points; drop i; do i=1 to 10; x=rannor(34343); y=rannor(12345); z=rannor(54321); output; end; run; The program creates the

Data Visualization
Dan Heath 0
A (tool)tip for band plots

Recently, I had a discussion with a user concerning the volume of imagemap data generated for an interactive,  web-based visual contain a large number of graphs. The large amount of imagemap data was causing problems with the current version of their web browser. The graphs consisted of either bar charts

Mike Gilliland 0
Forecasting fashion apparel

Ten years ago I spent some time in women's undergarments*, as Director of Forecasting at Sara Lee Intimate Apparel (now Hanesbrands).  Sure, it sounds glamorous -- product posters on our office walls, quarterly runway shows of new products, and partying with the full-figured Playtex models (some of whom were fuller than I figured). 

Rick Wicklin 0
Detecting outliers in SAS: Part 2: Estimating scale

In a previous blog post on robust estimation of location, I worked through some of the examples in the survey article, "Robust statistics for outlier detection," by Peter Rousseeuw and Mia Hubert. I showed that SAS/IML software and PROC UNIVARIATE both support the robust estimators of location that are mentioned

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3 things every elementary statistics student should know

We’ve just published Chris Olsen’s Teaching Elementary Statistics with JMP, which offers the latest research on best practices and how JMP can facilitate teaching statistics. To mark the book’s publication, we asked Chris to tell us the top three things every elementary statistics student should know. Statistics is about numbers

Mike Gilliland 0
For the love of forecasting

Love can make a person do bad, dangerous, stupid, and irresponsible things.  Love of country can make a politician stray from his wife. Love of Pepsi can make a pop musician lose his hair in a pyrotechnics-gone-bad commercial. Love of acting can make academy award winners accept starring roles in Ishtar. And for

Rick Wicklin 0
Explaining coincidence

I was on vacation when a family member sidled up to me. "Rick, you're a statistician..." he began. I knew I was in trouble. He proceeded to tell me the story of Joseph "Newsboy" Moriarty, a New Jersey mobster who rose to prominence and became known as the bookie who

Rick Wicklin 0
Constants in SAS

Statistical programmers often need mathematical constants such as π (3.14159...) and e (2.71828...). Programmers of numerical algorithms often need to know machine-specific constants such as the machine precision constant (2.22E-16 on my Windows PC) or the largest representable double-precision value (1.798E308 on my Windows PC). Some computer languages build these

Chris Hemedinger 0
Splitting hairs among the ranks

This morning I logged onto my e-mail at 6:45 AM to learn that SAS was ranked as the No. 3 Best Company to Work For. No. 3 is not as high as No. 1.  But it's very, very close.  Perhaps even barely distinguishable, in the larger scheme of things. I

Data Visualization
Dan Heath 0
They go where you put them

An issue that SAS/GRAPH users have wrestled with in the past has been how to put tick marks at irregular intervals on their axes. In PROC GPLOT, if you specify irregular intervals using the ORDER option on the AXIS statement, the procedure’s axis kicks into a “discrete” mode, where the

Rick Wicklin 0
Compute a running mean and variance

In my recent article on simulating Buffon's needle experiment, I computed the "running mean" of a series of values by using a single call to the CUSUM function in the SAS/IML language. For example, the following SAS/IML statements define a RunningMean function, generate 1,000 random normal values, and compute the

Analytics | SAS Events
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Our conference lineup for 2012

Here at SAS Publishing, we’ve started the new year off with a bang, particularly when it comes to conferences. We’re attending a number of new shows in addition to the usual lineup this year. Visit our booth, meet our authors, check out our new and forthcoming titles, and talk with

Rick Wicklin 0
Reading ALL variables INTO a matrix

The SAS/IML READ statement has a few convenient features for reading data from SAS data sets. One is that you can read all variables into vectors of the same names by using the _ALL_ keyword. The following DATA steps create a data set called Mixed that contains three numeric and

Rick Wicklin 0
Overlay density estimates on a plot

A recent question on a SAS Discussion Forum was "how can you overlay multiple kernel density estimates on a single plot?" There are three ways to do this, depending on your goals and objectives. Overlay different estimates of the same variable Sometimes you have a single variable and want to

Rick Wicklin 0
How to lie with a simulation

In my article on Buffon's needle experiment, I showed a graph that converges fairly nicely and regularly to the value π, which is the value that the simulation is trying to estimate. This graph is, indeed, a typical graph, as you can verify by running the simulation yourself. However, notice

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