Logistic Regression

Dr. Joseph Hilbe

Aim of Course:

Logistic regression is one of the most commonly-used statistical techniques. It is used with data in which there is a binary (success-failure) outcome (response) variable, or where the outcome takes the form of a binomial proportion. Like linear regression, one estimates the relationship between predictor variables and an outcome variable. In logistic regression, however, one estimates the probability that the outcome variable assumes a certain value, rather than estimating the value itself. This course will cover the functional form of the logistic model and how to interpret model coefficients. The concepts of "odds" and "odds ratio" are examined, as well as “risk ratio” and the difference between the two statistics. Our emphasis is on model construction, interpretation, and goodness of fit. We also discuss categorical response models, e.g ordered logistic and multinomial regression. Exercises include hands-on computer problems.

Who Should Take This Course:

Medical researchers, epidemiologists, forensic statisticians, environmental scientists, actuaries, data miners, industrial statisticans will all find this course useful. It is an essential course for anyone who needs to model data with binary or categorical outcomes, and who need to estimate probabilities of given outcomes based on predictor variables.

For those enrolled in Professional Advancement Programs, this is a required or elective course in the following Programs:

• Biostatistics (epidemiology) - required
• Biostatistics (controlled trials) - required
• Statistics in Business & Marketing - elective
• Data Mining - required
• 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 M. Hilbe is an Emeritus Professor at the University of Hawaii, and since 1992 has served as Adjunct Professor of Statistics at Arizona State University. In January 2007 he was also selected as a Solar System Ambassador by NASA's Jet Propulsion Laboratory at California Institute of Technology, a position he continues to hold. Among other journal editorships, he has been Software Reviews Editor for The American Statistician since 1997. 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 numerous published statistical procedures, book chapters, and journal articles, Dr. Hilbe authored 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). Dr. Hilbe is currently writing Logistic Regression Models (Chapman & Hall/CRC Press), which should be in press later in 2008.

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 10-15 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 Professional Advancement Program 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:

Apr. 25 - May. 23, 2008
Oct. 31 - Nov. 28, 2008
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 10-15 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

Prerequisite:

The equivalent of Introduction to Statistics I: Inference for a Single Variable, and Introduction to Statistics II: Working with Bivariate Data (and, if necessary before these courses, Introduction to Statistics for Beginners or Survey of Statistics for Beginners). Familiarity with standard multiple linear regression is helpful, It is covered briefly in the above introductory courses; for a more complete treatment, see Regression Analysis. Some familiarity with statistical software that can do logistic regression is desirable (see below).

Course Text:

The course text is Logistic Regression by Joseph Hilbe. The book has not yet been published, and will be provided in electronic form. (Note: prior sessions of this course were taught using Applied Logistic Regression by David W. Hosmer and Stanley Lemeshow, a standard reference on the topic, available from Wiley by clicking here. Wiley typically gives statistics.com customers a 15% discount at checkout time.)

Software:

Course participants may use any software that is capable of doing logistic regression. The instructor is most familiar with Stata, and the methods covered in this course will be illustrated in Stata. Click Here for information on obtaining a free (or nominal cost) copy of various software packages for use during the course.

Registration:

Register Online - $449
Register Online (academic) - $349 (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.

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.