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Advanced Analytics | Analytics

Renee NockerJuly 8, 2014 0

What is a better decision worth?

The word “analytics” is widely misused and misunderstood. While SAS arguably invented the advanced analytics and predictive analytics categories more than 38 years ago, other software vendors have used the term to describe things like reporting, monitoring, and tracking what happened. The value of these more simple capabilities are easily

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Advanced Analytics | Analytics

Mike GillilandJuly 3, 2014 0

Foresight/SAS webinar: forecasting on mobile devices

On July 24, 10am ET, Stavros Asimakopoulos delivers the next installment of the Foresight/SAS Webinar Series, "Forecasting 'In the Pocket': Mobile Devices Can Improve Collaboration." Based on his article (co-authored with George Boretos and Constantinos Mourlas) in the Winter 2014 issue of Foresight, Stavros will discuss how smartphones, tablet computers

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Advanced Analytics | Analytics

Mike GillilandJune 27, 2014 0

Forecasting across a time hierarchy with temporal reconciliation

The BFD blog probably won't make you the best forecaster you can be. There are plenty of books and articles and world class forecasting researchers (many of them meeting next week in Rotterdam at the International Symposium on Forecasting), that can teach you the most sophisticated techniques to squeeze every last

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Advanced Analytics

Annelies TjetjepJune 25, 2014 0

Treat Yourself with Text

Did you check your email and/or favourite social media site before leaving for work this morning? Did you do it before getting out of bed? Don’t worry, you’re not alone. I do it every morning as a little treat while I “wake up” and whether I realize it or not,

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Advanced Analytics

Udo SglavoJune 18, 2014 0

Combined forecasts: what to do when one model isn’t good enough

My esteemed colleague and recently-published author Jared Dean shared some thoughts on how ensemble models help make better predictions. For predictive modeling, Jared explains the value of two main forms of ensembles --bagging and boosting. It should not be surprising that the idea of combining predictions from more than one

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Advanced Analytics

Rick WicklinJune 18, 2014 0

Approximating a distribution from published quantiles

A colleague asked me an interesting question: I have a journal article that includes sample quantiles for a variable. Given a new data value, I want to approximate its quantile. I also want to simulate data from the distribution of the published data. Is that possible? This situation is common.

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Advanced Analytics | Analytics

Mike GillilandJune 13, 2014 0

Weather forecasts: deterministic, probabilistic or both?

In 1965's Subterranean Homesick Blues, Bob Dylan taught us: You don't need a weatherman / To know which way the wind blows In 1972's You Don't Mess Around with Jim, Jim Croce taught us: You don't spit into the wind By combining these two teachings, one can logically conclude that:

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Advanced Analytics

Rick WicklinJune 13, 2014 0

Geometry, sensitivity, and parameters of the lognormal distribution

Today is my 500th blog post for The DO Loop. I decided to celebrate by doing what I always do: discuss a statistical problem and show how to solve it by writing a program in SAS. Two ways to parameterize the lognormal distribution I recently blogged about the relationship between

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Advanced Analytics | Analytics

Mike GillilandJune 12, 2014 0

Guest blogger: Len Tashman previews Summer 2014 issue of Foresight

Our tradition from Foresight’s birth in 2005 has been to feature a particular topic of interest and value to practicing forecasters. These feature sections have covered a wide range of areas: the politics of forecasting, how and when to judgmentally adjust statistical forecasts, forecasting support systems, why we should

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Advanced Analytics | Learn SAS

Rick WicklinJune 9, 2014 0

How to generate a grid of points in SAS

In many areas of statistics, it is convenient to be able to easily construct a uniform grid of points. You can use a grid of parameter values to visualize functions and to get a rough feel for how an objective function in an optimization problem depends on the parameters. And

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Advanced Analytics

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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Advanced Analytics

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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Advanced Analytics

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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Advanced Analytics

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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Advanced Analytics

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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Advanced Analytics

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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Advanced Analytics

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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Advanced Analytics

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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Advanced Analytics

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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Advanced Analytics

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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