A 3-Day Remote Seminar Taught by
Paul D. Allison, Ph.D.
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
Starting April 22, we are offering this seminar as a 3-day synchronous*, remote workshop for the first time. Each day will consist of a 4-hour live lecture held via the free video-conferencing software Zoom. You are encouraged to join the lecture live, but will have the opportunity to view the recorded session later in the day if you are unable to attend at the scheduled time.
Each lecture session will conclude with a hands-on exercise reviewing the content covered, to be completed on your own. An additional session will be held Thursday and Friday afternoons as an “office hour”, where you can review the exercise results with the instructor and ask any questions.
*We understand that scheduling is difficult during this unpredictable time. If you prefer, you may take all or part of the course asynchronously. The video recordings will be made available within 24 hours of each session and will be accessible for two weeks after the seminar, meaning that you will get all of the class content and discussions even if you cannot participate synchronously.
MORE DETAILS ABOUT THE COURSE CONTENT
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 4,400 times.
Because this is a hands-on course, you will need to use a computer loaded with a recent version of SAS (release 9.2 or later) or Stata (release 13 or later).
Who should Register?
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.
- Review of linear model
- Dichotomous dependent variables in linear regression
- Odds and odds ratios
- The logistic (logit) regression model
- Estimating the logit model with Stata or SAS.
- Details of maximum likelihood estimation
- Interpreting logit coefficients
- Generalized R-square and other measures of fit
- Factor and class variables
- Hypothesis tests
- Probit model and other link functions
- Nonconvergence of ML estimates
- Logit analysis for contingency tables
- Multinomial response models : unordered case
- Logistic models for ordered polytomies
- Latent variable interpretation
- Response-based sampling
- GEE estimation
- Discrete choice models
“The Logistic Regression course gave me in-depth hands-on experience. I wish the class could have been longer!”
Lora Kasselman, City University of New York
“For a person with little background in Stata and very little knowledge about SAS, I think this course is relatively easy to follow. The booklet provided different examples and helped a lot in understanding the course. I would definitely recommend this course to those who just started to explore the data analysis field.”
Hoi Ting Wan (Cheryl), Northwestern University
“Dr. Paul Allison is a tremendous instructor and very clear in his teaching style. I found the Logistic Regression course built off of other statistical training and extended my understanding of analyses using categorical data.”
Andrea Johnson, Georgetown University
“I have attended several statistics courses in school and online courses but this course really clarified my concepts like no other. I loved the time to actually work on Stata during the course so that there was an opportunity for asking questions in real time. I would highly recommend this course!”
Shweta Gore, MGH Institute of Health Professions
“As a master’s student, this course was a good introduction to the real application of advanced statistical methods in complex analysis. The course investigated the inherent messiness of data, generating solutions to overcome any problems associated with them.”
Nicholas Bernardo, University of Rhode Island
“The course is exactly what I need to have a clear understanding of logistic regression. I’ve learned many useful commands to analyze the data. I’ve also learned how to precisely interpret the data. The instruction is clear, hands-on, with lots of useful tips and examples. Highly recommended for anyone who wants to have a thorough understanding of logistic regression.”
Xiaoli Yin, Baruch College