Wesley Murry
Dickinson College
Wireless data networks are becoming an integral part of the Internet,
expecially as a network access technology (i.e. PDA's, Laptops, etc).
In response, research has centered on extending the quality-of-service
(QoS) and fairness models for wired networks to wireless networks. My
research has centered on learning about packet scheduling algorithms in
the wireless network model. It is the hope that through analysis of these
algorithms a metric of fairness can be developed.
Bob Starbuck
A clinical trial of an anxiolytic agent will be presented to illustrate
some of the topics and issues that confront a statistician involved in
clinical research in the pharmaceutical industry. Topics that will be
covered include clinical trial protocol design, sample size estimation,
randomization, handling of missing values, grouping of data, data
analysis, and presentation of results. Career opportunities and success
factors will also be discussed.
Carol Wellington
A Bayesian belief network (BBN) is an artificial intelligence model used in
applications that require reasoning under uncertainty. While the models have
been hand-built successfully for a number of years, current research is
attempting to "learn" the model from historic data instead of relying on human
experts for model construction. After detailing the issues these learning
algorithms are designed to handle, I will show results of applying my learning
strategy to the prediction of stock prices. Now that I have models resulting
from various learning algorithms, I am working on how to evaluate learning
algorithms in this application. I will show the way learning algorithms have
traditionally been evaluated, why that is not particularly insightful in the
application and some other alternatives I have been investigating. In some
ways, the learned model could be considered to be a new technical analysis
tool, so the evaluation methods I am investigating could be applied to
evaluate and compare the usefulness of other technical analysis tools.
Thomas H. Short As an applied statistician, I have the opportunity to work on many
interesting and challenging analyses. One of my favorites involved
tracking cognitive development in Villanova Nursing students over the
their four years of college. An instrument known as the Learning
Contexts Questionnaire (LCQ) was supposed to be administered to each
student in each semester, but many of the values in the dataset are
missing. Without the missing values, I would have performed a standard
repeated measures analysis. The missing values forced me to think
creatively to develop an analysis that would be true to the data and
also understandable to my client.
Jim Wiseman The U.S. Constitution provides that seats in the House of
Representatives "shall be apportioned among the several states
according to their respective numbers," but doesn't give any details
on exactly how this is to be done. The history of apportionment
includes lots of political backbiting and surprisingly rich
mathematics. I'll answer such burning questions as "Why are there 435
members of the House?" and "How *are* they apportioned among the
states?"
Thomas L. Drucker As we enter the 21st century, it's worth looking back at a confluence
of factors that created a new discipline around 1900. The familiar geometry
of Euclid was being generalized to include geometries that stretched
familiar space in different directions. The points that had been
problematic for mediaeval philosophers talking about angels were now made
equivalent to functions. Finally, Georg Cantor was demonstrating that set
theory could be robbed of some of its mystery and domesticated for
mathematical use. Out of this mixture was born the discipline we call
set-theoretic topology. No background in set theory or topology is assumed
but we shall say a little about why the Erlangen program lasted longer than
most programs in the new television season are likely to.
William, a physics major at Dickinson, spent his summer doing an internship with the CA Internet startup SearchButton.com. He will tell us about his internship, how he found it and the types of things that he did and learned.
How are bees, ants, robots and wireless networks related? We'll explore the relationship between social insects, colonies of robots and the use of wireless networks for communication. We'll see how real ants solve shortest path problems and how this can help with network routing. Finally we'll look at a routing algorithm based on bee pollination that I developed for routing data in an Ad-Hoc wireless network.
12/1/00 - Anxiety Over Statistics (or Vice Versa?)
Wyeth-Ayerst
11/20/00 - Comparing and Evaluating Bayesian Belief Network Learning Algorithms When Applied to Predicting Stock Price
Department of Computer Science
Shippensburg University
Fri: 11/3/00 12:00 - Creatively Desperate (or Desperately Creative) Statistics: A Case Study of Repeated Measures Analysis with Sparse Data
Department of Mathematical Sciences
Villanova University
Thurs: 10/26/00 12:30 - `God help the state of Maine when mathematics reach
for her': integer allocation and the Alabama paradox.
Northwestern University
10/16/00 - Tea for Two and T2-Spaces: The Point of Point-Set Topology
9/25/00 - Being an Intern in .com World
William Menzie
Dickinson College
9/11/00 - Bees, Ants, Robots and Wireless Networks
Grant Braught
Dickinson College
Chats from previous semesters:
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| Fall 1999
| Spring 1999
| Fall 1998
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