Leonard A. Stefanski
Department of Statistics,
North Carolina State University,
Raleigh, NC, 27695-8203.
Tel: (919) 515-1945
- Ph.D. Statistics, 1984, University of North Carolina, Chapel Hill, NC.
- M.S. Statistics, 1983, University of North Carolina, Chapel Hill, NC.
- B.S. Mathematics, 1979, University of Connecticut, Storrs, CT.
- H.S.D., 1971, Waterford High School, Waterford, CT.
- J.H.S.D., 1967, Clark Lane Junior High School, Waterford, CT.
- E.S.C., 1966, Quaker Hill Elementary School, Quaker Hill, CT.
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Current Research Interests:
- Variable selection.
- Measurement error models.
- Generalized linear models.
- Environmental statistics.
- Trout distributions.
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Believe it or Not! You are looking at plots of residuals versus predicted values from the fit of multiple linear regression models to two different data sets.
Click on an image and see how it's done (alas, these are not naturally occurring 1-in-a-trillion-trillion-trillion chance random scatter plots).
Homer Simpson. Mathematics is no stranger to the Simpsons, see simpsonsmath.com,
but this may be the first time that Homer has dabbled in statistics. It appears that he
has embedded himself into an infinite do loop!
The image above is a spoof of a scene from a Simpson's episode in which Homer
tackles a difficult
mathematical derivation. Click on the image to learn how it was constructed.
Ronald Aylmer Fisher (1890-1962).
This is an embedded-image version of a well-known image of Fisher operating a calculating machine. You can find the original at
several places on the web,
see here for example.
Click on the image to learn how it was constructed.
Below is an animated illustration of a nifty feature
available in the new version of JMP software---interactive tuning
parameter selection for variable selection methods. The top-right
panel contains the forward-selection, BIC criterion selection curve for
a data set with the Homer Simpson image embedded in the residual plot
(n = 8406, p = 99, 4 nonzero).
As the tuning parameter moves right to left toward the minimum of
BIC curve, the corresponding parameter estimates can be tracked on the
coefficent path plots (top-left panel); and the resulting residual
plots appear in the lower-left panel. Thanks to JMP developer and
die-hard Wolfpack fan, Clay Barker,
for preparing the animation.
... and my favorite citations of it:
North Carolina lottery repeat numbers: (in a lovely introspective journal article written in the first person) Leonard A. Stefanski. "The North Carolina Lottery Coincidence," The American Statistician,
62.2 (2008): 130-134
Evil by Design: Interaction Design to Lead Us into Temptation by Chris Nodder.
A humorous story on a similar event in the North Carolina Lottery can be found in L. A. Stefanski,
"The North Carolina Lottery Coincidence," The American Statistician,
62 (2008): 130-134
Understanding Probability by Henk Tijms.
If you've stuck with me this far, you're probably really interested in the
subject matter! I would recommend the following article to gain additional
perspective: Stefanski, Leonard A. (2008). The North Carolina lottery coincidence.
The American Statistician. 62, 130-134. Stefanski reflects upon the different
interests of statisticians and laypersons (including the media) in understanding
rare occurrences involving lotteries, sports, and perhaps other phenomena.
Whereas laypersons seem to be interested in what Stefanski calls a "narrow"
perspective (e.g., Aaron Hill's achievement), statisticians embrace a "wide"
perspective, seeking to contextualize an occurrence in the larger set of
opportunities for an event to occur. Writes Stefanski: Statisticians should
point out when seemingly rare events are not really that rare. But in doing
so we should not lose sight of the fact that for some human interest stories,
a probability calculation from the "narrow perspective" is appropriate.
My hunch is that we sometimes do lose sight of the human-interest angle
because we are geared toward the "wide" perspective (p. 131).
The Hot Hand in Sports by Alan Reifman.
Some heavier reading ...
The shadowy images you see around in the background are large trout holding in about six feet of water.
ST552 Syllabus and Old Final Exams
R Programs for Visualizing the NRC Rankings Data
NRC Rankings Data.
From the WABAC Machine ...
Don't know what the WABAC Machine is?
The material on this web page is based upon work supported by the National Science
Foundation under Grant No. 0504283.
Any opinions, findings, and conclusions or recommendations expressed in this material are
those of the author and do not necessarily reflect the views of the
National Science Foundation.
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