Generalized Linear Models

Dr. Joseph Hilbe and Dr. James Hardin

Aim of Course:

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.

Who Should Take This Course:

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

For those enrolled in a Program of Advanced Statistical Studies, this is a required or elective course in the following Programs:

• Statistics for Social Sciences - elective
• Biostatistics (controlled trials) - elective

Course Program:

The course is structured as follows:

The Instructor:

Dr. Joseph Hilbe is Emeritus Professor at the University of Hawaii and Solar System Ambassador with NASA's Jet Propulsion Laboratory at California Institute of Technology. Since 1992 Prof. Hilbe has served as an Adjunct Professor of Statistics at Arizona State University. Prof Hilbe is an elected Fellow of the American Statistical Association, for which he was on the initial Health Policy Statistics section executive committee, and is an elected member (Fellow) of the prestigious International Statistical Institute, for which he is chair of the Astrostatistics committee and Network, the first global association of astrostatisticians. Dr. Hilbe has authored over one hundred journal articles and is currently on the editorial boards of a number of statistics journals. He was the first editor of the Stata Technical Bulletin (now Stata Journal) and from 1997-2009 was Software Reviews Editor for The American Statistician. Dr. Hilbe is author of both Logistic Regression Models(2009, Chapman & Hall/CRC) and Negative Binomial Regression (2007, Cambridge University Press), and, with James Hardin, is author of Generalized Estimating Equations (2003, Chapman & Hall/CRC) and Generalized Linear Models and Extensions (2001, 2007, Stata Press). His latest book is R for Stata Users (2010, Springer, with R. Muenchen).

Dr. James Hardin is a Research Associate Professor at the University of South Carolina. Co-author (with Joseph Hilbe) of Generalized Estimating Equations, Dr. Hardin is on the editorial board of The Stata Journal and is the developer of the Stata GEE command, and with Dr Hilbe is developer of the GLM command.

Organization of the Course:

The course takes place over the internet, at statistics.com. 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 is scheduled to take place over 4 weeks, and 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 and work through exercises. Discussion among participants is encouraged. The instructor will provide answers and comments.

Certificates and Grades:

You may be interested only in learning the material presented, and not be concerned with grades or certificates. Or you may be enrolled in a statistics.com Program in Advanced Statistical Studies that requires demonstration of proficiency in the subject, in which case your work will be assessed for purposes of issuing a grade. Or you may require only a "Certificate of Course Completion," along with professional development credit in the form of Continuing Education Units (CEU's). As you begin the class, you will be asked to specify your category.

Credit:

This course offers continuing education units (CEU's). For those successfully completing the course (generally this means marks of 50% or better on the homework), 5.0 CEU's and a certificate will be issued by statistics.com, upon request.

Dates:

Nov. 26 - Dec. 31, 2010
Click here to be notified of future course offerings.

Participants gain access to the online materials on the first day of the course, and typically spend about 15 hours per week (at their convenience). You retain full access to course materials, including discussion board, for two weeks after the course closing date.

Level:

Intermediate/Advanced

Prerequisite:

The equivalent of Introduction to Statistics 1: Inference for a Single Variable, and Introduction to Statistics 2: Working with Bivariate Data (and, if necessary before these courses, Introduction to Statistics for Beginners or Survey of Statistics for Beginners).

For additional information about course prerequisites, click here.

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.

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. For additional information about course prerequisites, click here.

Course Text:

James Hardin and Joseph Hilbe (2007), Generalized Linear Models and Extensions, second edition (not included in course price) here (when you order your copy, be sure to put 'GLM course' in the Company/University field of the order form). PLEASE ORDER YOUR COPY IN TIME FOR THE COURSE STARTING DATE.

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.

Registration:

Register Online - $499
Register Online (academic) - $399 (you must be affiliated with a college, university or high school)

Add $50 service fee if you require a prior invoice, or if you need to submit a purchase order or voucher, pay by wire transfer or EFT, or refund and reprocess a prior payment. Please use this printed registration form, for these and other special orders.

Consider registering for this course together with two other Modeling courses as part of our special 3 course package registration for tuition savings.

Note: Courses may fill up at any time and registrations are processed in the order in which they are received. Your registration will be confirmed for the first available course date, unless you specify otherwise.

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