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Rick Wicklin 0
The great Christmas gift exchange revisited

One aspect of blogging that I enjoy is getting feedback from readers. Usually I get statistical or programming questions, but every so often I receive a comment from someone who stumbled across a blog post by way of an internet search. This morning I received the following delightful comment on

Mike Gilliland 0
Tumbling dice

Mean Absolute Percent Error (MAPE) is the most commonly used forecasting performance metric, and for good reason, the most disparaged. When we compute the absolute percent error the usual way, as APE = | Forecast - Actual | / Actual this is undefined when Actual = 0.  It can also lead to

Chris Hemedinger 0
SAS-L: 25 years old and still spry

The popular mailing list for the SAS user community hits a milestone this weekend by turning 25. 25 is often referred to as the "silver anniversary", but for a quarter century SAS users have found gold among the messages in this list, which feature everything from questions and answers about

Rick Wicklin 0
On the median of the chi-square distribution

I was at the Wikipedia site the other day, looking up properties of the Chi-square distribution. I noticed that the formula for the median of the chi-square distribution with d degrees of freedom is given as ≈ d(1-2/(9d))3. However, there is no mention of how well this formula approximates the

Mike Gilliland 0
Why forecasts are wrong: Unforecastable demand

Sometimes you can't forecast worth a darn because something is just not forecastable. Being "unforecastable" doesn't mean you can't create a forecast, because you can always create a forecast.  It just means there is so much instability or randomness in your demand patterns that even sophisticated forecasting methods don't help

Analytics | Learn SAS
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What we learned from this year’s conferences

JSM, Miami Beach, FL, July 31–August 3 Miami Beach in August is hot. Ridiculously hot. Almost as hot as our preview copies at this show. Conference goers were extremely excited about a number of our upcoming statistics titles, including Customer Segmentation and Clustering Using SAS® Enterprise Miner™, Second Edition, by

Rick Wicklin 0
My upcoming Twi(n)tter-view

What do you call an interview on Twitter? A Tw-interview? A Twitter-view? Regardless of what you call it, I'm going to be involved in a "live chat" on Twitter this coming Thursday, 10NOV2011, 1:30–2:00pm ET. The hashtag is #saspress. Shelly Goodin (@SASPublishing) and SAS Press author recruiter Shelley Sessoms (@SSessoms)

Data Visualization
Sanjay Matange 0
Hello, World

Welcome to this new blog on data visualization at SAS. Our goal is to engage with you on a discussion about analytical and business graphics for reporting and interactive applications. Our primary focus will be on ODS Graphics and related topics, but we look forward to a lively discussion on all things

Mike Gilliland 0
Why forecasts are wrong: Untrained / inexperienced forecasters

Among the suitable-for-blog-publication-without-risking-my-job definitions of masochism is this: A willingness or tendency to subject oneself to unpleasant or trying experiences So to be a forecaster, must you also be a masochist? Few people enjoy the difficulties and degradation that go with being a forecaster, so few are willing to do it

Analytics
Vincent Talucci 0
Tower running

On September 10, 2001, I was attending a law enforcement conference in Atlantic City, NJ. While I have attended hundreds of similar meetings, this conference stands out for several reasons. First, and most obvious, it was the eve of the day where most of our lives were indelibly altered. Second,

Mike Gilliland 0
Why forecasts are wrong: Inadequate/unsound/misused software

A common mistake in bad or misused software is choosing a forecasting model based solely on the model’s “fit to history” (often referred to as “best fit” or “pick best” functionality). The software provides (or the forecaster builds) several competing models which are then evaluated against recent history. The model

Rick Wicklin 0
The UNIQUE-LOC trick: A real treat!

When you analyze data, you will occasionally have to deal with categorical variables. The typical situation is that you want to repeat an analysis or computation for each level (category) of a categorical variable. For example, you might want to analyze males separately from females. Unlike most other SAS procedures,

Mike Gilliland 0
Flash3: Report from Analytics2011 in Orlando

Of course, forecasting the stock market is not perfectly analogous to forecasting demand for a product.  The asking price for a stock is largely "anchored" by the price of its most recent trades.  While market values may appear to randomly drift up and down, or in a general direction, we generally

Mike Gilliland 0
Flash2: Report from Analytics2011 in Orlando

In this second of three flash reports from last week's Analytics2011 conference, we hear about a favorite topic of mine -- the relationship between demand volatility and forecastability. Rob Miller of Avantor Performance Materials, on Forecastability and Demand Volatility The "comet chart," illustrating the relationship between demand volatility and forecast

Rick Wicklin 0
Video: Calling R from the SAS/IML Language

In SAS/IML 9.22 and beyond, you can call the R statistical programming language from within a SAS/IML program. The syntax is similar to the syntax for calling SAS from SAS/IML: You use a SUBMIT statement, but add the R option: SUBMIT / R. All statements in the program between the

Learn SAS
Shelly Goodin 0
SAS author's tip: generating a dagger

This week's featured tip is from master SAS user Art Carpenter and his classic book Carpenter's Complete Guide to the SAS REPORT Procedure. In his review for the book, Rick Mitchell-senior systems analyst at Westat-said "I am green with envy for the newest generation of SAS programmers because I wish that I had had this book in

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