Logistic Regression Using SAS PHL2012

Philadelphia, PA
Monday, July 16, 2012
Logistic Regression Using SAS PHL2012
Monday, July 16, 2012 9:00 AM -
Friday, July 20, 2012 5:00 PM (Eastern Time)

Temple University Center City
1515 Market St.
Philadelphia, PA 19102
Map and Directions
Logistic Regression Using SAS

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

  • What's wrong with ordinary linear regression when the dependent variable is a dichotomy.
  • Why ordinary regression is sometimes OK.
  • The easy and intuitive way to interpret logit coefficients.
  • Why logit coefficients are inherently standardized.
  • How to analyze contingency tables with a logistic regression program.
  • How to compare logit coefficients across groups, while adjusting for unobserved heterogeneity.
  • Why chi-square statistics should sometimes be ignored.
  • Why logistic regression is usually preferred to probit regression.
  • When to use the complementary log-log model.
  • Why logistic regression models sometimes fail to converge--and what you can do about it.
  • How sampling on the dependent variable can give sometimes give you better estimates.
  • How to estimate and interpret a multinomial logit model.
  • How you can fit a multinomial model with a binary logit program.
  • How to choose among three different approaches to ordinal dependent variables.
  • How to control for all constant characteristics of individuals using panel data.
  • Why marginal tables are so important in log-linear analysis.
  • How to convert a log-linear model to a logit model and vice versa.

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.

The seminar meets Monday through Friday at Temple University Center City, 1515 Market Street, Philadelphia.

Here is a typical day's schedule:

9-12 Lecture
12-1 Lunch break
1-3 Lecture
3-5 Computing and consulting

Participants get a copy of the just-published, second edtion of Professor Allison's book, Logistic Regression Using SAS® . 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 $1495 includes all course materials. A block of rooms has been reserved at the Club Quarts Hotel Philadelphia . See the Lodging Tab at the top of this page for further details.

Course Outline

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
7. Nonlinearity
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 

Computing

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

For other courses offered by Statistical Horizons, go to www.statisticalhorizons.com.
 

Contact Information

  • Phone: 610-642-1941
    Fax: 419-818-1220
    Email: allison@statisticalhorizons.com

Payment Instructions

  • 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.

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