Happy New Year!
The CCASA
wishes
you a successful
2009!
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January Luncheon |
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Luncheon Announcement
Noon to 1:30PM
TUESDAY
January 20th, 2009
The East Bank Club
500 N Kingsbury, Chicago
60610
Please join us for another exciting
talk in the CCASA's 2008-2009 Luncheon
program!
Our January speakers are Max Berniker
and Ian Stevenson, who specialize in
physical medicine and rehabilitation at
Northwestern University. Their talk is
entitled:
People are very good at statistics-when
they do not think they are actually doing
statistics.
Abstract:
The world is
complex and variable, and our perception of it is
noisy. In a recent study we have formalized
motor adaptation as the process of optimally
inferring changes in the world and our bodies
given our observed motor errors. In the first
part of our talk, we will present results that
demonstrate our approach. This model makes
predictions that are consistent with a wide
range of experimental data from numerous
research groups. What's more, this approach
offers a principled explanation for motor
adaptation and generalization as the result
of an inference strategy for the nervous system.
In the second half of our talk, we will
briefly discuss how Bayesian statistics can
be used to understand how the brain "works".
Neurons communicate with each other using
spikes of electrochemical activity
(essentially point processes). Recently, we
have been using
Generalized Linear Models with regularization
to understand what causes a neuron to spike
and how neurons interact with one another.
Inferential statistics is becoming an
increasingly important tool in understanding
these complex, high-dimensional systems.
The February luncheon, will be held
on February 17, 2009, and the speaker will be
Krishnan Saranthan from United
Airlines. Dr Saranthan will present a talk
on the use of statistical modeling in airline
operations.
Plans for our future luncheons will be
included in our
upcoming announcements and in the Parameter.
Lunch is $30 for CCASA members, $35 for
nonmembers. Nonmembers, join the chapter for a
year for only $15 and get the discount plus
all the
other benefits of membership! As usual,
the Lucille Derrick Fund will purchase a
limited number of tickets for students who
wish to attend. If you are a student and
would like to take advantage of this offer,
please register online below, and contact
Lou Fogg, expressing your interest.
Click
here to register online
For any questions or concerns, please
contact:
Lou
Fogg, VP for Luncheons
Phone: 312-942-6239 or E-mail: louis_fogg@rush.
edu
Please Spread the
Word!
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Chicago Chapter ASA Workshop Announcement |
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Short Course on Longitudinal Data
Analysis
Presenter:
Don
Hedeker, University of Illinois-Chicago
Sponsored by the Chicago
Chapter Of the American Statistical
Organization.
Date: Friday, March 20th, 2009
Location: The University of Chicago's
Booth School
of Business
The Gleacher Center
450 North Cityfront Plaza Drive
Chicago, Illinois 60611-4316
Time:
8:30am-5pm
Course summary
The course will provide an introduction to
longitudinal data analysis using mixed
effects regression models, drawing on
material from the book Longitudinal Data
Analysis (Wiley, 2006) using the new SuperMix
statistical software program (a 15-day trial
edition of SuperMix is available at
www.ssicentral.com/supermix/index.html). The
focus will be on application of these models,
with direct application illustrated using
SuperMix.
In particular, the basic
mixed-effects regression model for continuous
outcomes will be introduced and described,
including use of polynomials for expressing
change across time, the multilevel
representation of the mixed model, treatment
of time-invariant and time-varying
covariates, and modeling of the
variance-covariance structure of the repeated
measures. It will be shown how these models
can allow for missing data across time in
terms of the outcome variable, thus
permitting analysis of subjects who have
incomplete data across time. Finally,
because categorical outcomes are common in
many research areas, description and
application of mixed-effects logistic
regression models will also be covered.
Attendees are encouraged to download the
trial software onto their laptops prior to
course, and to bring their laptops with them
to the course.
Registration information to follow.
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Did you know? |
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As I'm sure many of you know, the use of R as
the statistical tool of choice has grown
dramatically in the past couple of years.
The New York Times had an interesting piece
in the business section last Wednesday
touting the power of R. The article is below
for your perusal.
Data Analysts Captivated by R's
Power
By Ashlee Vance
Published: January 6, 2009
New York Times
To some people R is just the 18th letter of
the alphabet. To others, it's the rating on
racy movies, a measure of an attic's
insulation or what pirates in movies say.
R is also the name of a popular programming
language used by a growing number of data
analysts inside corporations and academia. It
is becoming their lingua franca partly
because data mining has entered a golden age,
whether being used to set ad prices, find new
drugs more quickly or fine-tune financial
models. Companies as diverse as Google,
Pfizer, Merck, Bank of America, the
InterContinental Hotels Group and Shell use
it.
But R has also quickly found a following
because statisticians, engineers and
scientists without computer programming
skills find it easy to use.
"R is really important to the point that it's
hard to overvalue it," said Daryl Pregibon, a
research scientist at Google, which uses the
software widely. "It allows statisticians to
do very intricate and complicated analyses
without knowing the blood and guts of
computing systems."
It is also free. R is an open-source program,
and its popularity reflects a shift in the
type of software used inside corporations.
Open-source software is free for anyone to
use and modify. I.B.M., Hewlett-Packard and
Dell make billions of dollars a year selling
servers that run the open-source Linux
operating system, which competes with Windows
from Microsoft. Most Web sites are displayed
using an open-source application called
Apache, and companies increasingly rely on
the open-source MySQL database to store their
critical information. Many people view the
end results of all this technology via the
Firefox Web browser, also open-source
software.
R is similar to other programming languages,
like C, Java and Perl, in that it helps
people perform a wide variety of computing
tasks by giving them access to various
commands. For statisticians, however, R is
particularly useful because it contains a
number of built-in mechanisms for organizing
data, running calculations on the information
and creating graphical representations of
data sets.
Some people familiar with R describe it as a
supercharged version of Microsoft's Excel
spreadsheet software that can help illuminate
data trends more clearly than is possible by
entering information into rows and
columns.
What makes R so useful - and helps explain
its quick acceptance - is that statisticians,
engineers and scientists can improve the
software's code or write variations for
specific tasks. Packages written for R add
advanced algorithms, colored and textured
graphs and mining techniques to dig deeper
into databases.
Close to 1,600 different packages reside on
just one of the many Web sites devoted to R,
and the number of packages has grown
exponentially. One package, called
BiodiversityR, offers a graphical interface
aimed at making calculations of environmental
trends easier
Another package, called Emu, analyzes speech
patterns, while GenABEL is used to study the
human genome.
The financial services community has
demonstrated a particular affinity for R;
dozens of packages exist for derivatives
analysis alone.
"The great beauty of R is that you can modify
it to do all sorts of things," said Hal
Varian, chief economist at Google. "And you
have a lot of prepackaged stuff that's
already available, so you're standing on the
shoulders of giants."
R first appeared in 1996, when the statistics
professors Ross Ihaka and Robert Gentleman of
the University of Auckland in New Zealand
released the code as a free software
package.
According to them, the notion of devising
something like R sprang up during a hallway
conversation. They both wanted technology
better suited for their statistics students,
who needed to analyze data and produce
graphical models of the information. Most
comparable software had been designed by
computer scientists and proved hard to
use.
Lacking deep computer science training, the
professors considered their coding efforts
more of an academic game than anything else.
Nonetheless, starting in about 1991, they
worked on R full time. "We were pretty much
inseparable for five or six years," Mr.
Gentleman said. "One person would do the
typing and one person would do the
thinking."
Some statisticians who took an early look at
the software considered it rough around the
edges. But despite its shortcomings, R
immediately gained a following with people
who saw the possibilities in customizing the
free software.
John M. Chambers, a former Bell Labs
researcher who is now a consulting professor
of statistics at Stanford University, was an
early champion. At Bell Labs, Mr. Chambers
had helped develop S, another statistics
software project, which was meant to give
researchers of all stripes an accessible data
analysis tool. It was, however, not an
open-source project.
The software failed to generate broad
interest and ultimately the rights to S ended
up in the hands of Tibco Software. Now R is
surpassing what Mr. Chambers had imagined
possible with S.
"The diversity and excitement around what all
of these people are doing is great," Mr.
Chambers said.
While it is difficult to calculate exactly
how many people use R, those most familiar
with the software estimate that close to
250,000 people work with it regularly. The
popularity of R at universities could
threaten SAS Institute, the privately held
business software company that specializes in
data analysis software. SAS, with more than
$2 billion in annual revenue, has been the
preferred tool of scholars and corporate
managers.
"R has really become the second language for
people coming out of grad school now, and
there's an amazing amount of code being
written for it," said Max Kuhn, associate
director of nonclinical statistics at Pfizer.
"You can look on the SAS message boards and
see there is a proportional downturn in
traffic."
SAS says it has noticed R's rising
popularity at universities, despite
educational discounts on its own software,
but it dismisses the technology as being of
interest to a limited set of people working
on very hard tasks.
"I think it addresses a niche market for
high-end data analysts that want free,
readily available code," said Anne H. Milley,
director of technology product marketing at
SAS. She adds, "We have customers who build
engines for aircraft. I am happy they are not
using freeware when I get on a jet."
But while SAS plays down R's corporate
appeal, companies like Google and Pfizer say
they use the software for just about anything
they can. Google, for example, taps R for
help understanding trends in ad pricing and
for illuminating patterns in the search data
it collects. Pfizer has created customized
packages for R to let its scientists
manipulate their own data during nonclinical
drug studies rather than send the information
off to a statistician.
The co-creators of R express satisfaction
that such companies profit from the fruits of
their labor and that of hundreds of
volunteers.
Mr. Ihaka continues to teach statistics at
the University of Auckland and wants to
create more advanced software. Mr. Gentleman
is applying R-based software, called
Bioconductor, in work he is doing on
computational biology at the Fred Hutchinson
Cancer Research Center in Seattle.
"R is a real demonstration of the power of
collaboration, and I don't think you could
construct something like this any other way,"
Mr. Ihaka said. "We could have chosen to be
commercial, and we would have sold five
copies of the software."
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Interesting Article... |
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Here's another piece highlighting the
importance of improving our math and science
education to keep our country competitive.
Thomas Friedman wrote in the New York
Times Opinion Page on Sunday expressing his
views on the upcoming $1 trillion economic
stimulus package. It's an interesting
read and once again emphasizes the critical
need for quantitative professionals.
An
excerpt:
"You see, even before the current financial
crisis, we were already in a deep competitive
hole - a long period in which too many people
were making money from money, or money from
flipping houses or hamburgers, and too few
people were making money by making new stuff,
with hard-earned science, math, biology and
engineering skills.
The financial crisis just made the hole
deeper, which is why our stimulus needs to be
both big and smart, both financially and
educationally stimulating. It needs to be
able to produce not only more shovel-ready
jobs and shovel-ready workers, but more
Google-ready jobs and Windows-ready and
knowledge-ready workers."
Click
here to read the article in it's entirety.
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A Letter from the Editor- Family Dinner Conversation
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I recently posted on my
blog, and wanted to share it with you all
as well.
Please share your comments. I am interested
to hear your feedback!
Just a few nights ago, during our family
dinner, the topic of the eighth-grade social
order arose. It seems my 13-year-old twins,
Jay and Becky, have a pretty clear
understanding of where they (and all of their
classmates) stand in the pecking order.
Becky said she resides somewhere in the
middle -abercrombie jeans and Ugg boots are
clear plusses, but being in the advanced math
group lowers her overall score. Jay said he's
on "the lower end" and Becky did nothing to
dispute this or buttress her twin.
Jay has been known as a "math geek" since
second grade. He is a terrible dresser, combs
his hair once a month (whether it needs it or
not), plays competitive chess and piano for
the jazz band, is two grades ahead in math
(where he is the top student, definitely a
social blunder), and programs his calculator
for fun. Apparently all that's keeping him
from plummeting to the very bottom of the
social heap are decent soccer skills and some
talent in track.
Fortunately, I was armed with information
from a timely Wall Street Journal piece
called Doing
the Math to Find Good Jobs, published the
very day of our family discussion. The
Journal reported that the best job in the
U.S. is (drum roll, please)
mathematician! In even more good news, two
closely related fields came in second and
third - actuary and statistician. These
standings are based in part on favorable
working conditions - an indoor environment
free of toxic fumes, with no heavy lifting
required. The quantitative sciences also
score high in terms of pay, low stress levels
(really?) and a good work-life balance.
I was able to reassure my "math geek" son
that though it may seem like he's on the
bottom social rung of eighth grade, with hard
work and a little luck, his skills and
talents will give him a quick elevator ride
to the top of the job stratum as an adult. As
Bill Gates once said: "Be nice to nerds.
Chances are you'll end up working for one."
He should know.
Let this reassure you as well, my analytical
friends, and revel in your career choice!
My best wishes to you and yours for a healthy
and prosperous 2009
Linda
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Editor |
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Editor: Linda Burtch (312) 629-2400
PARAMETER, newsletter of the Chicago Chapter of
the American Statistical Association, is
published 10
times a year as a service to its members. To
submit
material for publication, contact the Editor,
Linda Burtch, email:
[email protected]
PARAMETER provides a job listing service by
publishing Positions Available and Positions
Wanted,
the latter being free to Chapter members.
Companies may list positions for $75.
Contact
the Editor for more information.
For additional information about Chicago Chapter
ASA, please visit us on the web at:
www.ChicagoASA.org
Also, visit the National ASA
web site www.amstat.org.
Email change of address to:
[email protected]
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