Logistic Regression Using SAS
A 2-Day Seminar on Regression Methods for Categorical Dependent Variables
Taught by Paul D. Allison, Ph.D.
Logistic regression is one of the most widely used methods in statistical analysis. 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, and discrete choice models.
You’ll learn the basic syntax and many options for PROC LOGISTIC, the most versatile SAS procedure for doing logistic regression.
This is a hands-on course, so you should bring your own laptop loaded with a recent version of SAS. Power outlets will be provided at each seat.
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 multiple regression, as well as elementary statistical inference. But you do not need to know matrix algebra, calculus, or likelihood theory.
All examples and exercises will use SAS. No previous knowledge of SAS is assumed, however. Furthermore, nearly all the techniques taught in the course can be translated fairly easily to other packages. Lecture notes using Stata are available to registrants upon request.
Location, format, materials.
The seminar meets 9 a.m. to 5 p.m. on Thursday and Friday, June 6-7, at Temple University Center City, 1515 Market Street, Philadelphia.
Participants will 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 notetaking.
Registration and Lodging
The fee of $895 includes all course materials.
Lodging Reservation Instructions
A block of rooms has been reserved at the Club Quarters Hotel, 1628 Chestnut Street, Philadelphia, PA at a special rate of $137 for Standard Room per night. It is a short 5 minute walk to the seminar location. In order to guarantee rate and availability, call Club Quarters during business hours at 203.905.2100 no later than May 6. Please identify yourself by using Group Code STA605.
- Review of linear model
- Dichotomous dependent variables in linear regression
- Odds and odds ratios
- The logistic (logit) regression model
- Estimating the logit model with SAS.
- Details of maximum likelihood estimation
- Interpreting logit coefficients
- Generalized R-square and other measures of fit
- 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 interpreation
- Response-based sampling
- Longitudinal data
“For people who are interested in categorical data analyses, this course is a great option! This course covers a variety of methods to analyze categorical data. Thanks Paul.”
Keng-Yen Huang, New York University Medical Center
“I have a fairly strong background in quantitative analysis but wanted to reinforce what I knew. Not only did this course strengthen what I knew already, but I also learned a number of useful diagnostics along with some very good insights into modeling methodology.”
Peter Zaleski, Villanova University
“This was the best SAS/Stat course that I have attended. Paul Allison offered us a course with perfect mix of methodology and practical examples. Thank you very much Paul!”
Aihua Pu, Cardiac Services BC
“Dr. Allison makes learning SAS incredibly inviting. I had sporadic experience using SAS partly because I found it intimidating to learn. After this course, I am eagerly looking forward to return to my job to apply what I’ve learned and to continue learning. Thank you.”
Thelma Carrillo, Texas Tech University HSC
“This is a great course combining the statistical theory with examples implemented in SAS. Professor Allison explains the results in more details than in the lecture notes, and addresses questions very well. I will definitely consider taking some other courses offered in the future.”
Bo Gao, Key Bank