Advanced Logistic Regression

Advanced Logistic Regression

taught by Joe Hilbe

Aim of Course:

After taking this online course, "Advanced Logistic Regression" participants will be able to specify, implement and interpret the output of a variety of advanced logistic regression models. This course moves beyond the topics covered in "Logistic Regression" and covers a number of situations that call for logistic-based modeling, including a variety of ordered-categorical response (both proportional and non-proportional) models, multinomial models, panel models with fixed and random effects, GEE and quasi-least-squares models, multi-level models, survey logistic models, discriminant logistic models, skewed and penalized logistic regression, median unbiased estimation, Monte Carlo sampling, and exact logistic regression.

Course Program:WEEK 1 - Proportional Odds Models
  • Overview of binary logistic regression
  • Overview of binomial logistic regression
  • Proportional odds models
WEEK 2 - Multinomial Response Model
  • Ordered non-proportional models
  • Multinomial logistic regression
  • Multinomial probit regression
  • Alternative categorical response models
  • Marginal effects and discrete change
WEEK 3 - Panel and Mixed Models
  • Panel models
  • GEE/Quasi-least squares models
  • Fixed- and random-effects models
  • Multi-level models
WEEK 4 - Penalized and Exact Models
  • Survey models
  • Exact logistic regression
  • Penalized logistic regression
  • Monte Carlo sampling methods
  • Median unbiased estimation


The homework in this course consists of short answer questions to test concepts, guided exercises in writing code and guided data analysis problems using software.

In addition to assigned readings, this course also has example software codes, supplemental readings available online, and an end of course data modeling


Advanced Logistic Regression

Who Should Take This Course:
Researchers in medicine, other life sciences, business, social science, environmental science, engineering and other fields who need to predict or model 1/0 or "yes-no" binary type responses as well as models having categorical and proportional responses. Those who deal with classifying data into risk groups as well as those who handle longitudinal and clustered data will find the course valuable.
Though it is not required for practical applications of material in this course, some familiarity with calculus is helpful for a complete understanding of model development.
Organization of the Course:
Options for Credit and Recognition:
Course Text:

The course text is A Practical Guide to ogistic Regression by Joseph Hilbe.



No single software package is capable of doing all the models covered in this course, though Stata comes closest and is used for illustrations and the instructor is most familiar with Stata. Click Here for information on obtaining a free (or nominal cost) copy of various software packages for use during the course. R code is also supplied for many illustrations. The homework can effectively be done in Stata, R or SAS.

Note:  If you are planning to use R in this course and are not already familiar with it, please consider taking one of our courses where R is introduced from the ground up:  "Introduction to R: Data Handling,"  "Introduction to R: Statistical Analysis," or "Introduction to Modeling."  R has a learning curve that is steeper than that of most commercial statistical software.



To be scheduled.

Advanced Logistic Regression


To be scheduled.

Course Fee: $589

Do you meet course prerequisites? What about book & software? (Click here to learn more)

We have flexible policies to transfer to another course, or withdraw if necessary (modest fee applies)

Group rates: Click here to get information on group rates. 

First time student or academic? Click here for an introductory offer on select courses. Academic affiliation?  You may be eligible for a discount at checkout.

This course may be scheduled on a contract basis. Please contact to arrange.

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