Flexible, affordable statistics education.
Designed to help you master the software you need to enhance your skills and the practical experience you need to get ahead.
Designed to help you master the software you need to enhance your skills and the practical experience you need to get ahead.

This course deals with regression models for count data; i.e. models with a response or dependent variable data in the form of a count or rate. The course will cover Poisson regression, the foundation for modeling counts, as well as extensions and modifications to the basic model.
Instructor(s):Analysts and researchers in a wide variety of fields who are concerned with modeling counts and rates.
Dates: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.
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. Multiple course registrations may be entitled to tuition discounts; read more.
This course deals with regression models for count data; i.e. models with a response or dependent variable data in the form of a count or rate. A count is understood as the number of times an event occurs; a rate as how many events occur within a specific area or time interval. The course will cover Poisson regression, the foundation for modeling counts, as well as extensions and modifications to the basic model. Extensions are required when the assumptions underlying the Poisson model are violated. Negative binomial regression is the foremost method used to extend the Poisson model. Since Poisson assumptions are rarely met in practice, substantial attention will be devoted to the negative binomial model and its variants.
This course is a core requirement or elective in the following Program(s) in Advanced Statistical Studies (PASS):
Prerequisite(s):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.
Some familiarity with modeling - such as that provided in Regression Analysis or Logistic Regression will also be helpful. Categorical Data Analysis or its equivalent will also be helpful in acquainting participants with the type of data involved.
The course is structured as follows
This course takes place over the internet, at statistics.com 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 you will receive individual feedback on your homework answers.
As you begin the class, you will be asked to specify your category.
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 record of course completion will be issued by Statistics.com, upon request.
J. Hilbe's Negative Binomial Regression, 2nd edition (Cambridge Univ. Press). You should order the forthcoming (March 2011) Second Edition from Stata Press here or call 800-782-8272. It is available as an ebook here.
The methods covered in this course will be illustrated in both Stata and R. The majority of examples in the course text provide complete Stata code and output, but complete R code is also provided for all examples. All R data sets, functions and scripts used in the book are available in the COUNT package, that can be downloaded from CRAN. Stata code is provided on the publishers web site for the book. SAS and Excel data sets are provided as well.