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Rick Wicklin 0
An improved simulation of card shuffling

Last week I presented the GSR algorithm, a statistical model of a riffle shuffle. In the model, a deck of n cards is split into two parts according to the binomial distribution. Each piece has roughly n/2 cards. Then cards are dropped from the two stacks according to the number

Analytics
Chuck Ellstrom 0
The Two E's

Unless you’ve been living under a rock, you’ve heard about the budget problems running rampant across all levels of government. Federal, State and Local Governments are all facing historic budget shortfalls due to the economic crisis and decreased tax receipts. This has led to a much closer examination of services

Rick Wicklin 0
Funnel plots: An alternative to ranking

In a previous blog post, I showed how you can use simulation to construct confidence intervals for ranks. This idea (from a paper by E. Marshall and D. Spiegelhalter), enables you to display a graph that compares the performance of several institutions, where "institutions" can mean schools, companies, airlines, or

Rick Wicklin 0
A statistical model of card shuffling

I recently returned from a five-day conference in Las Vegas. On the way there, I finally had time to read a classic statistical paper: Bayer and Diaconis (1992) describes how many shuffles are needed to randomize a deck of cards. Their famous result that it takes seven shuffles to randomize

Analytics
0
Getting to the point of return on analytics

Linking business analytics to economic value is a hard problem. Despite all the smarts that get poured into models, it's hard to tie them to financial measures such as profitability. And, because of that, it's hard to justify investment in analytics. Need headcount? Sorry, try again. Need tools? Sorry, can't

Rick Wicklin 0
The sound of the Dow...in SAS

At the beginning of 2011, I heard about the Dow Piano, which was created by CNNMoney.com. The Dow Piano visualizes the performance of the Dow Jones industrial average in 2010 with a line plot, but also adds an auditory component. As Bård Edlund, Art Director at CNNMoney.com, said, The daily

Rick Wicklin 0
Computing the variance of each column of a matrix

In a previous blog post about computing confidence intervals for rankings, I inadvertently used the VAR function in SAS/IML 9.22, without providing equivalent functionality for those readers who are running an earlier version of SAS/IML software. (Thanks to Eric for pointing this out.) If you are using a version of

Rick Wicklin 0
How to rank values

When comparing scores from different subjects, it is often useful to rank the subjects. A rank is the order of a subject when the associated score is listed in ascending order. I've written a few articles about the importance of including confidence intervals when you display rankings, but I haven't

Rick Wicklin 0
How to sample from independent normal distributions

In my article on computing confidence intervals for rankings, I had to generate p random vectors that each contained N random numbers. Each vector was generated from normal distribution with different parameters. This post compares two different ways to generate p vectors that are sampled from independent normal distributions. Sampling

SAS Events
Chris Hemedinger 0
SAS programmers: meet Twitter

"Twitter, thou art nought but data." So sayeth the SAS programmer. Many data analysts now recognize Twitter for what it is: a tremendous source of data covering almost any topic, from Justin Bieber's hair to political uprisings to technical conferences to company brands. SAS offers sophisticated solutions to harness this

Advanced Analytics
Rick Wicklin 0
Ranking with confidence: Part 1

I recently posted an article about representing uncertainty in rankings on the blog of the ASA Section for Statistical Programmers and Analysts (SSPA). The posting discusses the importance of including confidence intervals or other indicators of uncertainty when you display rankings. Today's article complements the SSPA post by showing how

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