### GLM

Generalized Linear Models

taught by James Hardin

Aim of Course:

This online course, "Generalized Linear Models" will explain the theory and background of generalized linear models (GLMs). More importantly, the course will describe how to apply these models to data, assess the model, and interpret the results. If you understand GLMs,you understand linear regression, logistic regression, Poisson regression, negative binomial regression, gamma regression, multinomial regression and so many other models that are either directly included in GLMs or are simple extensions. Finally, random effects models and generalized estimating equation (GEE) models are built on top of GLMs, so understanding GLMs is a great introduction to these advanced subjects!

This course may be taken individually (one-off) or as part of a certificate program.

Course Program:

## WEEK 1: General Overview of GLM

- Derivation of GLM functions
- GLM algorithms: OIM, EIM
- Fit and residual statistics

## WEEK 2: Continuous Response Models

- Gaussian
- Log-normal
- Gamma
- Log-gamma models for survival analysis
- Inverse Gaussian

## WEEK 3: Discrete Response Models

- Binomial models: logit, probit, cloglog, loglog, others
- Count models: Poisson, negative binomial, geometric

## WEEK 4: Problems with Overdispersion

- Overview of ordered and unordered logit and probit regression
- Overview of panel models

HOMEWORK:

Homework in this course consists of short answer questions to test concepts, guided data analysis problems using software, guided data modeling problems using software, and end of course data modeling project.

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

Note: The Institute gratefully acknowledges the contributions of Prof. Joseph Hilbe to the development of this course.

# Generalized Linear Models

Who Should Take This Course:

Analysts in any field who need to move beyond standard multiple linear regression models for modeling their data.

Level:

Intermediate/Advanced

The first week of the course presents theory to support the applications covered in weeks 2-4. If you are interested only in the applications, you can skim over the material in week 1. If you wish to follow along with week 1's development of theory, the following additional prerequisites apply:

Organization of the Course:

This course takes place online 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.

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.

Time Requirement:

About 15 hours per week, at times of your choosing.

Credit:

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

- You may be interested only in learning the material presented, and not be concerned with grades or a record of completion.
- 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.
- 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, CEU's and a record of course completion will be issued by The Institute, upon request.

Course Text:

*Generalized Linear Models and Extensions*, third edition by James Hardin and Joseph Hilbe. (When you order your copy, be sure to put 'GLM course at Statistics.com' in the Company/University field of the order form.)

Software:

In some lessons, you will benefit from being able to implement models in a software program that is able to do GLM (for example, Stata, SAS, S-PLUS, R). Click Here for information on obtaining a free (or nominal cost) copy of various software packages for use during the course.

Stata: The instructor is familiar with Stata. If you are undecided about which software to use, Stata, which is relatively easy to learn and use, is a safe choice.

R: If you want to use R with this course, you should have some prior experience and facility with it (tutorial help from the instructor or TA will be available but limited.) If you wish to use R, but no have current expertise in it, you should consider taking one of our introductory R courses before taking this one.

SAS: The TA can offer limited assistance with SAS in this course. If you want to use SAS with this course, you should have some prior experience and facility with it. If you wish to use SAS, but no have current expertise in it, you should consider taking an introductory course or courses from SAS Institute or elsewhere.

SPSS: The instructor and TA are not familiar with SPSS. If you want to use SPSS with this course, you should have some prior experience and facility with it. If you wish to use SPSS, but no have current expertise in it, you should consider taking an introductory course or courses from SPSS.

# Generalized Linear Models

April 14, 2017 to May 12, 2017April 13, 2018 to May 11, 2018