Generalized Linear Models

taught by James Hardin
and Joseph Hilbe

This course will explain the theory of generalized linear models (GLM), outline the algorithms used for GLM estimation, and explain how to determine which algorithm to use for a given data analysis. GLM allows the modeling of responses, or dependent variables, that take the form of counts, proportions, dichotomies (1/0), positive continuous values, as well as values that follow the normal Gaussian distribution. Logistic, Poisson, and negative binomial regression models are three of the most noteworthy GLM family members.

Note:  Detailed study of model specification and the interpretation of software output is handled in statistics.com's individual courses on regression, logistic regression, count data modeling, etc., and in the  Categorical Modeling course.

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

Generalized Linear Models

taught by James Hardin
and Joseph Hilbe

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Generalized Linear Models

taught by James Hardin
and Joseph Hilbe

This course will explain the theory of generalized linear models (GLM), outline the algorithms used for GLM estimation, and explain how to determine which algorithm to use for a given data analysis.

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

• Environmental Science

• Social Science

• Using R

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

This course does involve the presentation of theory, and requires familiarity with multiple linear regression. For a more complete coverage of regression, and to gain greater comfort with the presentation of theory, see Regression Analysis and Logistic Regression.

SESSION 1: General Overview of GLM

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

SESSION 2: Continuous Response Models

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

SESSION 3: Discrete Response Models

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

SESSION 4: Problems with Overdispersion

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

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.

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.

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.

Generalized Linear Models and Extensions, forthcoming third edition by James Hardin and Joseph Hilbe.  Order it here (when you order your copy, be sure to put 'GLM course at Statistics.com' in the Company/University field of the order form). PENDING PUBLICATION OF THE THIRD EDITION, COURSE PARTICIPANTS WILL BE PROVIDED WITH RELEVANT COURSE MATERIALS ELECTRONICALLY.

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.

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