Advanced Logistic Regression

Dr. Joseph Hilbe

Aim of Course:

After taking this course, participants will be able to specify, implement and interpret the output of a variety of advanced logistic regression models. This course moves beyond the topics covered in "Logistic Regression" and covers a number of situations that call for logistic-based modeling, including a variety of ordered-categorical response (both proportional and non-proportional) models, multinomial models, panel models with fixed and random effects, GEE and quasi-least-squares models, multi-level models, survey logistic models, discriminant logistic models, skewed and penalized logistic regression, median unbiased estimation, Monte Carlo sampling, and exact logistic regression.

Who Should Take This Course:

Researchers in medicine, other life sciences, business, social science, environmental science, engineering and other fields who need to predict or model 1/0 or "yes-no" binary type responses as well as models having categorical and proportional responses. Those who deal with classifying data into risk groups as well as those who handle longitudinal and clustered data will find the course valuable.

For those enrolled in a Program of Advanced Statistical Studies, this is a required or elective course in the following Programs:

• Biostatistics (epidemiology) - required
• Biostatistics (controlled trials) - elective
• Statistics in Business & Marketing - elective
• Data Mining - elective
• Statistics for Social Sciences - elective
• Statistics for Environmental Science - elective
• Engineering Statistics - elective

Course Program:

The course is structured as follows

The Instructor:

Dr. Joseph Hilbe is Emeritus Professor at the University of Hawaii and Solar System Ambassador with NASA's Jet Propulsion Laboratory at California Institute of Technology. Since 1992 Prof. Hilbe has served as an Adjunct Professor of Statistics at Arizona State University. Professor Hilbe is currently on the editorial boards of seven academic journals in statistics, and from 1997-2009 was Software Reviews Editor for The American Statistician. Professor Hilbe is an elected Fellow of both the American Statistical Association and Royal Statistical Society, and is an elected member (Fellow) of the International Statistical Institute. An author of over one hundred journal articles, and numerous published statistical procedures and book chapters, Dr. Hilbe is author of Logistic Regression Models (2009, Chapman & Hall/CRC) and Negative Binomial Regression (2007, Cambridge University Press), and, with James Hardin, is author of Generalized Estimating Equations (2003, Chapman & Hall/CRC) and Generalized Linear Models and Extensions (2001, 2007, Stata Press). He is also co-author of the forthcoming books, R for Stata Users (Springer, with R. Muenchen), and Quasi-Least Squares Regression (Chapman & Hall/CRC, with J. Shults).

Organization of the Course:

The course takes place over the internet, at statistics.com. During each course week, you participate at times of your own choosing - there are no set times when you must be online. Course participants will be given access to a private discussion board. In class discussions led by the instructor, you can post questions, seek clarification, and interact with your fellow students and the instructor. The course is scheduled to take place over 4 weeks, and typically requires 15 hours per week. At the beginning of each week, you receive the relevant material, in addition to answers to exercises from the previous session. During the week, you are expected to go over the course materials and work through exercises. Discussion among participants is encouraged. The instructor will provide answers and comments.

Certificates and Grades:

You may be interested only in learning the material presented, and not be concerned with grades or certificates. Or you may be enrolled in a statistics.com Program in Advanced Statistical Studies that requires demonstration of proficiency in the subject, in which case your work will be assessed for purposes of issuing a grade. Or you may require only a "Certificate of Course Completion," along with professional development credit in the form of Continuing Education Units (CEU's). As you begin the class, you will be asked to specify your category.

Credit:

This course offers continuing education units (CEU's). For those successfully completing the course (generally this means marks of 50% or better on the homework), 5.0 CEU's and a certificate will be issued by statistics.com, upon request.

Dates:

Oct. 30 - Nov. 27, 2009
Click here to be notified of future course offerings.

Participants gain access to the online materials on the first day of the course, and typically spend about 15 hours per week (at their convenience). You retain full access to course materials, including discussion board, for two weeks after the course closing date.

Level:

intermediate/advanced

Prerequisite:

The equivalent of Introduction to Statistics 1: Inference for a Single Variable, and Introduction to Statistics 2: Working with Bivariate Data (and, if necessary before these courses, Introduction to Statistics for Beginners or Survey of Statistics for Beginners). Participants should also have taken the course Logistic Regression or have an equivalent level of statistical expertise as covered in that course.

Course Text:

The course text is Logistic Regression Models by Joseph Hilbe, which you can order from CRC Press, or by using this form. CRC Press typically gives students a generous discount when students order the text using the above form (not by ordering the text online). Be sure to purchase your copy of the text prior to the course starting date.

Software:

No single software package is capable of doing all the models covered in this course, though Stata comes closest and is used for illustrations and the instructor is most familiar with Stata. Click Here for information on obtaining a free (or nominal cost) copy of various software packages for use during the course. R code is also supplied for many illustrations. The homework can effectively be done in Stata, R or SAS.

Registration:

Register Online - $469
Register Online (academic) - $369 (you must be affiliated with a college, university or high school)

Add $50 service fee if you require a prior invoice, or if you need to submit a purchase order or voucher, pay by wire transfer or EFT, or refund and reprocess a prior payment. Please use this printed registration form, for these and other special orders.

Consider registering for this course together wih two other Modeling courses as part of our special 3 course package registration for tuition savings.

Note: Courses may fill up at any time and registrations are processed in the order in which they are received. Your registration will be confirmed for the first available course date, unless you specify otherwise.

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