A five-day seminar on the analysis of discrete data using SAS, taught by Paul D. Allison, Ph.D.
Here are a few of the things you'll learn in this seminar
Who should attend?
If you need to analyze categorical data 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.
The course will stress generalized regression models with categorical dependent variables.
The course does emphasize SAS rather heavily. 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.
Location, format, materials.
seminar meets Monday through Friday at Temple University Center City, 1515 Market Street, Philadelphia.
Here is a typical day's schedule:
12-1 Lunch break
3-5 Computing and consulting
1. Traditional methods for categorical data analysis
2. Review of linear model
3. OLS regression with categorical covariates
4. Dichotomous dependent variables in OLS regression
5. Heteroscedasticity and nonnormality
6. Weighted least squares estimates of linear probability model
8. The logit model
9. Estimating the logit model with grouped data
10. Estimating the logit model with ungrouped data
11. Maximum likelihood estimation
12. Interpreting logit coefficients
13. Similarities with multiple regression
14. Nonconvergence of ML estimates
15. Probit model and other link functions
16. Latent variable interpretation of the models
17. Unobserved heterogeneity
18. Logit and probit analysis for contingency tables
19. Goodness-of-fit tests for grouped data
20. Multinomial response models : unordered case
21. Logit models for ordered polytomies
22. Uniform association model
23. Cumulative logit and probit
24. Continuation ratio models
25. Latent variable interpreation
26. Response-based sampling
27. Poisson regression
28. Panel data
29. Random effects models
30. GEE estimation
31. Fixed effects logit model
32. Event history models
33. Discrete choice models
The course will focus on four SAS® procedures for categorical data analysis: LOGISTIC, SURVEYLOGISTIC, GENMOD, and GLIMMIX. At least one hour each day is devoted to carefully structured and supervised assignments in a computer lab. Additional time is available for exploring other sample data sets. Or you can bring your own data and try out new techniques as you learn them.
Comments from Previous Participants
"Paul Allison’s instruction is much like his textbooks. He distills complex statistical concepts into much more digestable form. His frequent use of examples ensures that students learn how to apply what they learn. As a graduate level quantitative methods instructor, I would recommend this course to my students and colleagues."
Melanie Hughes, University of Pittsburgh
"This is a great course. It enabled me to learn different types of models and how to apply them to solve business issues. I really liked the fact this course enables one to quickly build models and explains what kind of statistics to check. This is very different from courses at school which focuses more on theory and less on applications. I am also grateful for the SAS code example provided in the class that greatly aids in model development."
Vijay Raghavan, Forest Labs
"The course covers lots of procedures relevant to categorical data and was presented in a chronological manner. You will really gain understanding of logistic regression in all aspects from dichotomous to polychotomous events. I will surely recommend this course to those who only know ordinary regressions."
Julie Mojica, Winnipeg Regional Health Authority
"Excellent course! Dr. Allison explains complex things in a very simple way. Thanks a lot!"
Joanna Collantes, Visa, Inc.
"I especially liked being able to practice what I learned with my own data set. I also liked having lab time immediately after lectures to reinforce what was taught. Reviewing it the next morning reinforced the concepts yet again. Thanks."
Laurie Cohen, Rutgers University
"Professor Allison provided rare and practical insight into the benefits and challenges associated with logistic regression."
Barbara Lamberton, University of Hartford
"The course was very useful, particularly regarding the variety of examples given, along with the code required to run the different types of analysis. Most people learn by examples as opposed to pure theory. This course presented a very nice balance of both."
Bilal Karriem, Keystats Inc.
"Logistical Regression Using SAS is a deep, thorough and practical course. I benefited a lot from the mix of theory and applications. Now, I can comfortably progress with the credit Possibility of Default (PD) modeling with additional skills to validate current methodologies. After this course, I decided to attend the Survival Analysis Course with Paul next year. I strongly recommend this course."
Kusay Alkhunaizi, Saudi Credit Bureau SIMAH
The fee for each course is $1495, which covers all course materials.
All major credit cards are accepted.
Our Tax ID number is 26-4576270.