Advanced Logistic Regression

Brief Description:

After taking this course, participants will be able to specify, implement and interpret the output of a variety of advanced logistic regression models not covered in the first course, "Logistic Regression."

Instructor(s):
Level: intermediate/advanced

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.

Dates:
April 26, 2013 to May 24, 2013
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Registration:
Please read the syllabus tab, noting the prerequisites, text and software requirements.

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Advanced Logistic Regression

Aim of Course:

After taking this course, 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.

This course is a core requirement or elective in the following Program(s) in Analytics and Statistical Studies (PASS):

• Biostatistics - Controlled Trials

• Biostatistics - Epidemiology

• Business Analytics

• Engineering Statistics

• Environmental Science

• Social Science

Prerequisite(s):

• Logistic Regression

Though it is not required for practical applications of material in this course, some familiarity with calculus (see statistics.com's brief Calculus Review course) is helpful for a complete understanding of model development.

Course Program:

SESSION 1

• Overview of binary logistic regression
• Overview of binomial logistic regression
• Proportional odds models

SESSION 2

• Ordered non-proportional models
• Multinomial logistic regression
• Multinomial probit regression
• Alternative categorical response models
• Marginal effects and discrete change

SESSION 3

• Panel models
• GEE/Quasi-least squares models
• Fixed- and random-effects models
• Multi-level models

SESSION 4

• Survey models
• Exact logistic regression
• Penalized logistic regression
• Monte Carlo sampling methods
• Median unbiased estimation

Organization of the Course:

This course takes place over the internet at the Institute for 4 weeks. During each course week, you participate at times of your own choosing - there are no set times when you must be online. Course participants will be given access to a private discussion board. In class discussions led by the instructor, you can post questions, seek clarification, and interact with your fellow students and the instructor.

The course typically requires 15 hours per week. At the beginning of each week, you receive the relevant material, in addition to answers to exercises from the previous session. During the week, you are expected to go over the course materials, work through exercises, and submit answers. Discussion among participants is encouraged. The instructor will provide answers and comments, and at the end of the week, you will receive individual feedback on your homework answers.

Credit:
Students come to the Institute for a variety of reasons. As you begin the course, you will be asked to specify your category:

1. You may be interested only in learning the material presented, and not be concerned with grades or a record of completion.
2. You may be enrolled in PASS (Programs in Analytics and Statistical Studies) that requires demonstration of proficiency in the subject, in which case your work will be assessed for a grade.
3. You may require a "Record of Course Completion," along with professional development credit in the form of Continuing Education Units (CEU's).  For those successfully completing the course, 5.0 CEU's and a record of course completion will be issued by The Institute, upon request.

Course Text:

The course text is Logistic Regression Models by Joseph Hilbe, which you can order from CRC Press, or by using this form. CRC Press typically gives students a generous discount when students order the text using the above form (not by ordering the text online).

PLEASE ORDER YOUR COPY IN TIME FOR THE COURSE STARTING DATE.

Software:

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.

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Advanced Logistic Regression

Instructor(s):
Dates:
April 26, 2013 to May 24, 2013
Course Fee: $499
Academic Rate: $399

Before registering, please read the syllabus tab, noting the prerequisites, text and software requirements. When you click the register button, you will be taken to our secure transaction page.

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