Logistic Regression

A 2-Day Seminar Taught by Paul D. Allison, Ph.D.

Read reviews of this seminar

To see a sample of the course materials, click here.

Logistic regression is by far the most widely used statistical method for the analysis of categorical data. In this seminar, you’ll learn virtually everything you need to know to become a skilled user of logistic regression. We’ll cover the theory and practice of binary logistic regression in great detail including topics such as

  • odds and odds ratios
  • maximum likelihood estimation
  • interpretation of coefficients
  • convergence failures
  • goodness of fit
  • contingency table analysis
  • response-based sampling

We’ll also cover more advanced topics including ordered logistic regression, multinomial logistic regression, discrete-choice analysis, and methods for analyzing clustered data.

This is a hands-on course with lots of exercises to help you master the material. Both SAS and Stata will be used for all examples and exercises. 

Professor Allison is the author of Logistic Regression Using SAS which is now in its second edition and has been cited more than 3900 times.  


Because this is a hands-on course, you will need to bring your own laptop loaded with a recent version of SAS (release 9.2 or later) or Stata (release 13 or later).

Seminar participants who are not yet ready to purchase Stata could take advantage of StataCorp’s free 30-day evaluation offer or their 30-day software return policy.

There is now a free version of SAS, called the SAS University Edition, that is available to anyone. It has everything needed to run the exercises in this course, and it will run on Windows, Mac or Linux computers. However, you do need a 64-bit machine with at least 1 GB of RAM. You also have to download and install virtualization software that is available free from third-party vendors. The SAS Studio interface runs in your browser, but you do not have to be connected to the Internet. The download and installation are a bit complicated, but well worth the time and effort.

Who should attend?

If you need to analyze categorical outcomes and have a basic statistical background, this course is for you. You should have a good working knowledge of the principles and practice of linear regression, as well as elementary statistical inference. But you do not need to know matrix algebra, calculus, or likelihood theory. Some experience with either SAS or Stata is highly desirable.

Location, Format and materials

The class will meet from 9 am to 5 pm each day with a 1-hour lunch break at Temple University Center City, 1515 Market Street, Philadelphia, PA 19103. 

Participants receive a bound manual containing detailed lecture notes (with equations and graphics), examples of computer printout, and many other useful features. This book frees participants from the distracting task of note taking. 

Registration and Lodging

The fee of $995.00 includes all seminar materials.

Refund Policy

If you cancel your registration at least two weeks before the course is scheduled to begin, you are entitled to a full refund (minus a processing fee of $50). 

Lodging Reservation Instructions

A block of guest rooms has been reserved at the Club Quarters Hotel, 1628 Chestnut Street, Philadelphia, PA at a special rate of $165 per night. This location is about a 5-minute walk to the seminar location. In order to make reservations, call 203-905-2100 during business hours and identify yourself by using group code SH0404 or click here. For guaranteed rate and availability, you must reserve your room no later than Monday, March 4, 2019.

If you make reservations after the cut-off date ask for the Statistical Horizons room rate (do not use the code) and they will try to accommodate your request.


  1. Review of linear model
  2. Dichotomous dependent variables in linear regression
  3. Odds and odds ratios
  4. The logistic (logit) regression model
  5. Estimating the logit model with Stata or SAS.
  6. Details of maximum likelihood estimation
  7. Interpreting logit coefficients
  8. Generalized R-square and other measures of fit
  9. Factor and class variables
  10. Hypothesis tests
  11. Probit model and other link functions
  12. Nonconvergence of ML estimates
  13. Logit analysis for contingency tables
  14. Multinomial response models : unordered case
  15. Logistic models for ordered polytomies
  16. Latent variable interpretation
  17. Response-based sampling
  18. GEE estimation
  19. Discrete choice models

Comments from Recent participants

“This is an excellent course for those in practice or research. The content used and knowledge gained is applicable across broad settings and the breadth of experience of fellow attendees augments the learning experience. The course is highly interactive, tailored to learning needs and the pace is perfect. The statistical and theoretical expertise shared was the true benefit of the course, how to operationalize this using specific software was an added advantage. Finally, the interpretation of findings is invaluable as a nurse researcher in training.”
  Amanda Hessels, Rutgers University, College of Nursing

“I came to the course with mostly informal knowledge of logistical regression and came out with a strong grasp of both basic techniques and also tricks and pitfalls; all explained very clearly.”
  Merlin Chowkwanyun

“I have been working with Logistics Regression using SAS over a year. There are so many options/tricks in SAS to help build the model. This course enables me to improve upon logistic model building using various skills/options/tricks I learned. This course not only discusses how to read the result but also the math behind it. Paul is an excellent teacher and willing to take questions during or after class. Even if you have some logistic regression skills, I highly recommend this course.”
  Liang Pan, 21st Century Insurance  

“Having in-person access to a seasoned expert like Dr. Allison is invaluable to model builders in both the public and private sector. Both seasoned and novice modelers can learn a lot from his class.”
  Mary Zenker, Key Bank 

“I have been using Stata for logistic regression but had never had a chance to systematically learn it. This workshop is very thorough with just the right amount of technicality. Professor Alison is very clear in explaining the materials.”
  Lihua Wang, San Francisco State University 

“Dr. Allison’s teaching style is very clear. His teaching materials are extremely logical, clear and well thought out. This course made both logistic regression and SAS modeling clearer to me.”
  Lydia Pace 

“This is a wonderful course. Professor Allison does not train you in the method, he will teach you the method. This translates into leaving the class with a skill set that you can confidently use going forward.”
  Patia McGrath, University of Pennsylvania, Wharton

“Paul Allison’s expertise on Stata and logistic models is extremely beneficial for any researcher. This class was immensely helpful for my research in finance.”
  Jordan Nott, Federal Reserve Bank 

“I found the course energizing – especially for someone that is retooling, following time in administration at the university. It both reviewed basics and built upon those basics to greatly extend understanding.”
  Renata Forste, Brigham Young University 

“Paul Allison delivers the perfect thorough and thoughtful boot camp. The in-depth notes provide a take home road map on theory, examples, and code that will immediately get you up and running and they will keep you there.”
  Thomas Kraemer, Alloy Investments