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. Exercises include hands-on computer problems.

Who Should Take This Course:

Medical researchers, epidemiologists, forensic statisticians, environmental scientists, actuaries, data miners, industrial statisticans, sports statisticians, and fisheries, to name a few, 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 a Program of Advanced Statistical Studies, 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
• 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:

Mar. 12 - Apr. 9, 2010
Sep. 10 - Oct. 8, 2010
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

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). 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 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:

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 primarily be illustrated in Stata. Nearly all Stata commands, however, have corresponding R code at the end of each chapter. Click Here for information on obtaining a free (or nominal cost) copy of various software packages for use during the course.

Stata: The instructor is familiar with Stata and the illustrations and assignments are fully integrated with Stata. If you are undecided about which software to use, Stata, which is relatively easy to learn and use, is a safe choice.

R: R-language solutions to assignments will be provided in this course, and R code is provided at the end of the chapters in the text duplicating nearly all Stata examples used in the text. R code for and tutorial help from the instructor or TA will be available but limited. If you want to use R with this course, you should have some prior experience and facility with it. If you wish to use R, but no have current expertise in it, you should consider taking one of our introductory R courses before taking this one.

SAS: The instructor and TA can offer limited assistance with SAS in this course. If you want to use SAS with this course, you should have some prior experience and facility with it. If you wish to use SAS, but no have current expertise in it, you should consider taking an introductory course or courses from SAS Institute or elsewhere.

SPSS: The instructor can offer limited assistance with SPSS, but there is no TA support. While SPSS is easier to use than R or SAS for the purposes of this course, we nonetheless recommend that if you want to use SPSS with this course, you should have some prior experience and facility with it. If you wish to use SPSS, but no have current expertise in it, you should consider taking an introductory course or courses from SPSS.

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 with 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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