Jim Albert's Home Page

BOOKS/SOFTWARE:

Bayesian Computation Using Minitab

Ordinal Data Modeling (with Val Johnson)

Software for Ordinal Data Modeling

CLASS WEB PAGES:

MATH 115 - Introduction to Statistics

MATH 115B - Introduction to Statistics - Baseball Emphasis

Academic History:

I received my bachelor degree in math from Bucknell University in 1975 and my doctoral degree in statistics from Purdue University in 1979. I have been a professor in the Department of Mathematics and Statistics at Bowling Green State University since 1979. I've had two sabbaticals: 1986-87 at the University of Southampton (England) and 1994-95 at Duke University.

My Academic Interests:

Click here if you want to see a list of my publications and technical reports.

• Bayesian inference.
  Bayesian statistics is based on two ideas. First, uncertainty about unknown quantities is expressed using the language of subjective probability, and, given new information or data, probabilities are updated using Bayes rule. I believe that Bayesian inferential methods have advantages over classical statistical procedures for a wide range of inferential problems. I'm particularly interested in developing inferential procedures for the class of generalized linear models. Recently, I've written papers on how to check the goodness of a model from a Bayesian viewpoint.
• The analysis of sports data.
  I've been a baseball fan for many years -- I follow the Phillies when they are playing well. (I don't have to follow them that often!) Baseball is especially interesting for me, due the large amount of data that is collected about the game. I've written articles on how to interpret baseball data.

• Can hitters be streaky? (In other words, can one distinguish hitting data from coin tossing?)
• Does the "best" team always win the World Series?
• Is there any information in breakdown data (hitting statistics that are broken down by home/away, grass/turf, day/night game, etc.)?
• Teaching statistics.
  I believe that we can do a much better job of communicating the basic notions of statistics in a beginning course. We are currently teaching elementary statistics class using classroom activities and a Bayesian perspective and the results are encouraging. I've recently written a package of software (Minitab macros) that can be used in teaching Bayesian inference at an undergraduate level.

Hobbies:

I've always enjoyed tennis, although my playing time is currently restricted to the summer months. I'm starting to coach soccer which is fun. Likewise, music has always been enjoyable for me -- I grew up with the Beatles, the Band and the Moody Blues and I haven't left the 60's yet. I enjoy sports -- especially baseball (when the Phillies have a winning season) and college basketball.

Some courses I teach:

In the fall semester of odd-number years, I teach Math 447/547 - Exploratory Data Analysis. This is a fun course in which we learn a number of interesting methods for exploring data. (The students have to stop me when I talk too much about baseball data!)

In the spring semester of even-number years, I teach Math 648 - Bayesian Inference. The students learn the basic components of the Bayesian viewpoint towards inference. The contents of this course change every time I teach it, due to the rapid growth in the theory and application of Bayesian methods. All statisticians should take this!

I currently supervise Math 115 - Introduction to Statistics. This is no longer the course that everyone hates to take. We focus on the basic concepts of learning from data. The student will become a more intelligent consumer of statistical information.

We recently have introduced an activity-style for teaching MATH 115. I have written activities and web material for introducing data analysis, probability, and inference by Bayes' rule.

A new course I've developing is Math 341 - Principles of Probability and Statistics . Did you know that all Ohio public schools require that probability and statistics topics be taught in all grades K-12? This class will use materials developed by the American Statistical Association in their Quantitative Literacy program for teachers. We'll use the computer as a tool to discover concepts in probability and statistics.

Places I visit on the Web:

• The Institute of Statistics and Decision Sciences at Duke University. This is a great starting point for more links to statistics servers.
• CNNSI - a great sports page (especially for you baseball fans that want good box scores).
• BGSU - more about my home institution.
• Journal of Statistical Education - a electronic journal on teaching statistics.