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Rick WicklinJune 4, 2014 0

Simulate lognormal data with specified mean and variance

In my book Simulating Data with SAS, I specify how to generate lognormal data with a shape and scale parameter. The method is simple: you use the RAND function to generate X ~ N(μ, σ), then compute Y = exp(X). The random variable Y is lognormally distributed with parameters μ

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Advanced Analytics | Analytics

Mike GillilandJune 3, 2014 0

Upcoming forecasting conferences

We're entering the busy season for forecasting events, and here is the current calendar: Analytics2014 - Frankfurt The European edition of Analytics2014 kicks off tomorrow in Frankfurt, Germany. Five hundred of the leading thinkers and doers in the analytics profession hook up for two full days of interaction and learning.

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Rick WicklinMay 12, 2014 0

How to create a string of a specified length in SAS/IML

In my recent post on how to understand character vectors in SAS/IML, I left out an important topic: How can you allocate a character vector of a specified length? In this article, "length" means the maximum number of characters in an element, not the number of elements in a vector.

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Rick WicklinMay 7, 2014 0

This article is actually fastidious: How spammers generate random comments for blogs

Last week Chris Hemedinger posted an article about spam that is sent to SAS blogs and discussed how anti-spam software helps to block spam. No algorithm can be 100% accurate at distinguishing spam from valid comments because of the inherent trade-off between specificity and sensitivity in any statistical test. Therefore,

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Advanced Analytics | Learn SAS

Rick WicklinMay 5, 2014 0

Blanks and lengths: Understanding SAS/IML character vectors

SAS programmers are probably familiar with how SAS stores a character variable in a data set, but how is a character vector stored in the SAS/IML language? Recall that a character variable is stored by using a fixed-width storage structure. In the SAS DATA step, the maximum number of characters

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Michael ThomasMay 2, 2014 0

Big data in your face

If you've followed technology news in the last few weeks, you've probably seen a proliferation of awestruck faces wearing blacked out goggles, similar to the image I'm using here. The goggles are Oculus Rift immersive VR headsets, and the news is the $2 billion dollar acquisition of Oculus by Facebook.

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examples of mixed rice

Jared DeanMay 1, 2014 0

How ensemble models help make better predictions

My oldest son is in the school band, and they are getting ready for their spring concert. Their fall concert was wonderful; hearing dozens of students with their specific instruments playing together creates beautiful, rich sounding music. The depth of sound from orchestral or symphonic music is unmatched. In data

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Advanced Analytics | Analytics

Mike GillilandApril 30, 2014 0

A naive forecast is not necessarily bad

As we saw in Steve Morlidge's study of forecast quality in the supply chain (Part 1, Part 2), 52% of the forecasts in his sample were worse than a naive (random walk) forecast. This meant that over half the time, these companies would have been better off doing nothing and

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Advanced Analytics | Analytics | Customer Intelligence

Carl FarrellApril 28, 2014 0

Baskets full of benefits from analytics

With as much as I travel, I have to confess that I’ve become a bit of a food snob. And for good reason, I might add. Two days in a Chilean hospital will make anyone stick with what they know and trust. After that experience, it was just me and

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Advanced Analytics | Analytics

Mike GillilandApril 24, 2014 0

Forecast quality in the supply chain (Part 2)

As we saw last time with Steve Morlidge's analysis of the M3 data, forecasts produced by experts under controlled conditions with no difficult-to-forecast series still failed to beat a naive forecast 30% of the time. So how bad could it be for real-life practitioners forecasting real-life industrial data? In two words:

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Advanced Analytics | Analytics

Mike GillilandApril 23, 2014 0

Forecast quality in the supply chain (Part 1)

The Spring 2014 issue of Foresight includes Steve Morlidge's latest article on the topic of forecastability and forecasting performance. He reports on sample data obtained from eight business operating in consumer (B2C) and industrial (B2B) markets. Before we look at these new results, let's review his previous arguments: 1. All

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Rick WicklinApril 21, 2014 0

Local functions (not!) in the SAS/IML language

I have previously written about the scope of local and global variables in the SAS/IML language. You might wonder whether SAS/IML modules can also have local scope. The answer is no. All SAS/IML modules are known globally and can be called by any other modules. Some object-oriented programming languages support

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Advanced Analytics | Analytics

Mike GillilandApril 17, 2014 0

Guest blogger: Len Tashman previews Spring 2014 issue of Foresight

Here is editor Len Tashman's preview of the new Spring 2014 issue of Foresight. In particular note the new article by Steve Morlidge of CatchBull, reporting on an analysis of eight B2B and B2C companies, which we'll discuss in a separate post. An organization’s collaboration in forecasting and planning has

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Advanced Analytics | Analytics

Mike GillilandApril 16, 2014 0

Q&A with Steve Morlidge of CatchBull (Part 4)

Q: How would you set the target for demand planners: all products at 0.7? All at practical limit (0.5)? A: In principle, forecasts are capable of being brought to the practical limit of an RAE of 0.5. Whether it is sensible to attempt to do this for all products irrespective

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Hao ChenApril 2, 2014 0

How Bayesian analysis might help find the missing Malaysian airplane

At the time this blog entry was written, there still appears to be little to no signs of locating the missing Malaysian flight MH370. The area of search, although already narrowed down from the size of the United States at one point to the size of Poland, is still vast

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Rick WicklinApril 2, 2014 0

A different way to interpret the negative binomial distribution

While at SAS Global Forum 2014 I attended a talk by Jorge G. Morel on the analysis of data with overdispersion. (His slides are available, along with a video of his presentation.) The Wikipedia defines overdispersion as "greater variability than expected from a simple model." For count data, the "simple

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Advanced Analytics | Machine Learning

Jared DeanMarch 21, 2014 0

March Madness and Predictive Modeling

In my region of North Carolina (Raleigh, Durham, and Chapel Hill) one of the most anticipated times of the year has arrived— the NCAA basketball tournament. This is a great time of year for me, because I get to combine several of my passions. For those who don’t live among crazed college

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Rick WicklinMarch 19, 2014 0

Define functions with optional parameters in SAS/IML

Last month I blogged about defining SAS/IML functions that have default parameter values. This language feature, which was introduced in SAS/IML 12.1, enables you to skip arguments when you call a user-defined function. The same technique enables you to define optional parameters. Inside the function, you can determine whether the

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Advanced Analytics | Learn SAS

Rick WicklinMarch 17, 2014 0

Finding elements in one vector that are not in another vector

The SAS/IML language has several functions for finding the unions, intersections, and differences between sets. In fact, two of my favorite utility functions are the UNIQUE function, which returns the unique elements in a matrix, and the SETDIF function, which returns the elements that are in one vector and not

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Rick WicklinMarch 14, 2014 0

For pi day: A continued fraction expansion of pi

Many geeky mathematical people celebrate "pi day" on March 14, because the date is written 3/14 in the US, which is evocative of the decimal representation of π = 3.14.... Most people are familiar with the decimal representation of π. The media occasionally reports on a new computational tour-de-force that

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Rick WicklinFebruary 24, 2014 0

Three ways to add a smoothing spline to a scatter plot in SAS

Like many SAS programmers, I use the Statistical Graphics (SG) procedures to graph my data in SAS. To me, the SGPLOT and SGRENDER procedures are powerful, easy to use, and produce fabulous ODS graphics. I was therefore surprised when a SAS customer told me that he continues to use the

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Advanced Analytics | Learn SAS

Rick WicklinFebruary 19, 2014 0

Techniques for scoring a regression model in SAS

My previous post described how to use the "missing response trick" to score a regression model. As I said in that article, there are other ways to score a regression model. This article describes using the SCORE procedure, a SCORE statement, the relatively new PLM procedure, and the CODE statement.

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Advanced Analytics | Learn SAS

Rick WicklinFebruary 17, 2014 0

The missing value trick for scoring a regression model

A fundamental operation in statistical data analysis is to fit a statistical regression model on one set of data and then evaluate the model on another set of data. The act of evaluating the model on the second set of data is called scoring. One of first "tricks" that I

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Advanced Analytics | Data Management | Data Visualization

David PopeFebruary 15, 2014 0

Does the architecture matter as much as the analytics?

I was recently part of team discussing enterprise architecture with a chief IT architect, and we were explaining how SAS can integrate into their existing infrastructure, add business value on top it and even fit into their future planned infrastructure. This conversation was one of the reasons I blogged about

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Advanced Analytics | SAS Events | Students & Educators

Polly Mitchell-GuthrieFebruary 7, 2014 0

Skills needed for competitive advantage in analytics (hint, it's not just the math)

I'm a big believer in both/and thinking, so I'll stand squarely in the middle and say that the most important skills for competitive advantage in analytics include a combination of top-notch modeling abilities along with business acumen, critical thinking, and curiosity. I was intrigued by a blog post on this

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Kenneth SanfordJanuary 28, 2014 0

Using panel data to measure the economic impact of the Super Bowl

This year the Super Bowl will take place in East Rutherford, New Jersey at MetLife Stadium just outside New York City. For the first time in this event’s 48-year history, the game will take place outdoors in a cold-weather environment, potentially subjecting players and fans to sub-freezing temperatures. The fans

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Rick WicklinJanuary 21, 2014 0

The best articles of 2013: Twelve posts from The DO Loop that merit a second look

I began 2014 by compiling a list of 13 popular articles from my blog in 2013. Although this "People's Choice" list contains many articles that I am proud of, it did not include all of my favorites, so I decided to compile an "Editor's Choice" list. The blog posts on

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Rick WicklinJanuary 7, 2014 0

13 popular articles from 2013

In 2013 I published 110 blog posts. Some of these articles were more popular than others, often because they were linked to from a SAS newsletter such as the SAS Statistics and Operations Research News. In no particular order, here are some of my most popular posts from 2013, organized

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Rick WicklinDecember 4, 2013 0

Secret Santa: What is the probability that someone pulls her own name from a hat?

Each year my siblings choose names for a Christmas gift exchange. It is not unusual for a sibling to pick her own name, whereupon the name is replaced into the hat and a new name is drawn. In fact, that "glitch" in the drawing process was a motivation for me

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Gerhard SvolbaDecember 3, 2013 0

"The hungry statistician" – or why we never can get enough data

As the “Year of Statistics” comes to a close, I write this blog in support of the many statisticians who carefully fulfil their analysis tasks day by day, and to defend what may appear to be demanding behavior when it comes to data requirements. How do statisticians get this reputation? Are we

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