Tag: Statistical Programming

Rick Wicklin 8
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

Rick Wicklin 5
Simulation of Buffon's needle in SAS

Buffon's needle experiment for estimating π is a classical example of using an experiment (or a simulation) to estimate a probability. This example is presented in many books on statistical simulation and is famous enough that Brian Ripley in his book Stochastic Simulation states that the problem is "well known

Rick Wicklin 4
New 2012 resolutions for my blog

Hello, 2012! It's a New Year and I'm flushed with ideas for new blog articles. (You can also read about The DO Loop's most popular posts of 2011.) The fundamental purpose of my blog is to present tips and techniques for writing efficient statistical programs in SAS. I pledge to

Rick Wicklin 4
Recoding a character variable as numeric

The other day someone posted the following question to the SAS-L discussion list: Is there a SAS PROC out there that takes a multi-category discrete variable with character categories and converts it to a single numeric coded variable (not a set of dummy variables) with the character categories assigned as

Rick Wicklin 23
Funnel plots for proportions

I have previously written about how to create funnel plots in SAS software. A funnel plot is a way to compare the aggregated performance of many groups without ranking them. The groups can be states, counties, schools, hospitals, doctors, airlines, and so forth. A funnel plot graphs a performance metric

Rick Wicklin 12
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

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