Modeling Count Data

Modeling Count Data

This course will teach you regression models for count data, models with a response or dependent variable data in the form of a count or rate, Poisson regression, the foundation for modeling counts, and extensions and modifications to the basic model.

This course will teach you regression models for count data, models with a response or dependent variable data in the form of a count or rate, Poisson regression, the foundation for modeling counts, and extensions and modifications to the basic model.

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Overview

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 the nature of various count models, problems of over- and under-dispersion, fit and residual tests, and graphics for count models. It also looks at advanced count models and an overview of Bayesian count models.  

Learning Outcomes

Students who complete this course will start with the fundamentals of modeling counts and move on to explore assessment of fit, alternative count models, and more advanced count models. They will study a broad range of topics designed to help them understand key model assumptions, how to select appropriate models and how to interpret model outcomes.

 

  • Fit Poisson models to count data
  • Interpret coefficients and rates
  • Test for and deal with overdispersion
  • Fit alternate models for count data – negative binomial and variants
  • Model underdispersion

Who Should Take This Course

Analysts and researchers in a wide variety of fields who are concerned with modeling counts and rates.

Instructors

dr-james-hardin

Dr. James Hardin

Dr. James Hardin is an Associate Dean of Faculty Affairs and Curriculum 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 the developer of the GLM command.

See Instructor Bio

Course Syllabus

Week 1

Fundamentals of Modeling Counts; Poisson Regression

  • What are counts
  • Understanding a statistical count model
  • Variety of count models
  • Estimation - the modeling process
  • Poisson model assumptions
  • Apparent overdispersion
  • The basic Poisson mode
  • Interpreting coefficients and rate ratios
  • Exposure; modeling time, area, and space
  • Prediction
  • Poisson marginal effects

Week 2

Overdispersion, Assessment of Fit, and Negative Binomial Regression

  • Count model fit statistics
  • Overdispersion: what, why, and how
  • Testing overdispersion
  • Methods for handling overdispersion - adjusting SEs
  • Analysis of residuals
  • Likelihood ratio tests
  • Model selection criterion
  • Validation sample
  • Varieties of negative binomial models
  • Negative binomial model assumptions
  • Examples using real data

Week 3

Alternative Count Models: NB Fit Tests, PIG, Problem with Zeros

  • General negative binomial fit tests
  • Generalized NB-P regression (NBP)
  • Heterogeneous negative binomial  (NBH)
  • Generalized Poisson - modeling underdispersion (GP)
  • Poisson inverse Gaussian (PIG)
  • Zero-truncated count models
  • Two-part hurdle models
  • Zero-inflated count models

Week 4

Underdispersed Count Data, Advanced Count Models

  • Generalized Poisson - modeling underdispersion
  • Exact Poisson regression
  • Truncation and censored count models
  • Finite mixture models
  • Non-parametric and quantile count models
  • Overview of longitudinal and clustered count models
  • 3-parameter count models
  • Overview of Bayesian count models
  • Project preparation

Class Dates

2021

Oct 22, 2021 to Nov 19, 2021

2022

Oct 21, 2022 to Nov 18, 2022

2023

No classes scheduled at this time.

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Prerequisites

Recommended

We recommended, but do not require as eligibility to enroll in this course, an understanding of the material covered in these following courses.

Regression Analysis

This course will teach you how multiple linear regression models are derived, assumptions in the models, how to test whether data meets assumptions, and develop strategies for building and understanding useful models.
Topic: Statistics, Statistical Modeling | Skill: Intermediate | Credit Options: ACE, CAP, CEU
Class Start Dates: Jan 15, 2021, May 7, 2021, Oct 1, 2021

What Our Students Say​

I found this a very good grounding on theory, but also there were good discussions about practical issues. The introduction to the various software packages was also very helpful.

Jim Pearse
Director, Health Policy Analysis

The instructor is fantastic and it was a pleasure to interact with him throughout the duration of the course.

Digant Gupta
Life Sciences and Clinical Research

Frequently Asked Questions

Can I transfer or withdraw from a course?

We have a flexible transfer and withdrawal policy that recognizes circumstances may arise to prevent you from taking a course as planned. You may transfer or withdraw from a course under certain conditions.

  • Students are entitled to a full refund if a course they are registered for is canceled.
  • You can transfer your tuition to another course at any time prior to the course start date or the drop date, however a transfer is not permitted after the drop date.
  • Withdrawals on or after the first day of class are entitled to a percentage refund of tuition.

Please see this page for more information.

Who are the instructors at the Institute?

The Institute has more than 60 instructors who are recruited based on their expertise in various areas in statistics. Our faculty members are:

  • Authors of well-regarded texts in their area;
  • Advisory board members;
  • Senior faculty; and
  • Educators who have made important contributions to the field of statistics or online education in statistics.

The majority of our instructors have more than five years of teaching experience online at the Institute.

Please visit our faculty page for more information on each instructor at The Institute for Statistics Education.

Please see our knowledge center for more information.

What type of courses does the Institute offer?

The Institute offers approximately 80 courses each year. Topics include basic survey courses for novices, a full sequence of introductory statistics courses, bridge courses to more advanced topics. Our courses cover a range of topics including biostatistics, research statistics, data mining, business analytics, survey statistics, and environmental statistics.

Please see our course search or knowledge center for more information.

Do your courses have for-credit options?

Our courses have several for-credit options:

  • Continuing education units (CEU)
  • College credit through The American Council on Education (ACE CREDIT)
  • Course credits that are transferable to the INFORMS Certified Analytics Professional (CAP®)

Please see our knowledge center for more information.

Is the Institute for Statistics Education certified?

The Institute for Statistics Education is certified to operate by the State Council of Higher Education for Virginia (SCHEV). For more information visit: http://www.schev.edu

Please see our knowledge center for more information.

Visit our knowledge base and learn more.

FAQs + Knowledge Base

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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.
Topic: Statistics, Statistical Modeling | Skill: Intermediate, Advanced | Credit Options: CEU
Class Start Dates: Jun 18, 2021

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Class Start Dates: Jul 16, 2021, Jan 21, 2022

Additional Course Information

Organization of 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 Requirements

This is a 4-week course requiring 10-15 hours per week of review and study, at times of your choosing.

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 in the course.

Course Text

The required text is Modeling Count Data, Hilbe, Joseph M (2014), Cambridge University Press. This paperback edition includes R, Stata, SAS and Excel/CVS code, which can be downloaded from the author’s website. R data and functions are located in the COUNT package on CRAN. An electronic version of the book is also available from the publisher, or on Amazon.

Software

The methods covered in this course are handled well by Stata, R and for the most part, SAS.  Data sets used in the text are available in Stata, R SAS and Excel formats. With respect to code and output:

Stata
Code and output are provided for all examples for which known Stata commands exist.

R
Functions and scripts are available in the COUNT and msme packages. Additional R code is provided at the following web site:  http://works.bepress.com/joseph_hilbe/ Most examples have R support.

SAS
Some code and output is provided, e.g., chapter 15 on Bayesian count models.

The instructor and TA are familiar with Stata and R. The instructor is familiar with most SAS procedures related to the modeling of count data. No instructional support is available for SAS.  If you plan on using R and are not already familiar with it, please consider taking one of our courses where R is introduced from the ground up:  R-Programming: Introduction,” “Introduction to R: Data Handling,” or “Introduction to R: Statistical Analysis.” R has a learning curve that is steeper than that of most commercial statistical software.

Software Uses and Descriptions | Available Free Versions
To learn more about the software used in this course, or how to obtain free versions of software used in our courses, please read our knowledge base article “What software is used in courses?” 

Course Fee & Information

Enrollment
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.

Transfers and Withdrawals
We have flexible policies to transfer to another course or withdraw if necessary.

Group Rates
Contact us to get information on group rates.

Discounts
Academic affiliation?  In most courses you are eligible for a discount at checkout.

New to Statistics.com?  Click here for a special introductory discount code.  

Invoice or Purchase Order
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.

Options for Credit and Recognition

This course is eligible for the following credit and recognition options:

No Credit
You may take this course without pursuing credit or a record of completion.

Mastery or Certificate Program Credit
If you are enrolled in mastery or certificate program that requires demonstration of proficiency in this subject, your course work may be assessed for a grade.

CEUs and Proof of Completion
If you require a “Record of Course Completion” along with professional development credit in the form of Continuing Education Units (CEU’s), upon successfully completing the course, CEU’s and a record of course completion will be issued by The Institute upon your request.

INFORMS-CAP
This course is recognized by the Institute for Operations Research and the Management Sciences (INFORMS) as helpful preparation for the Certified Analytics Professional (CAP®) exam and can help CAP® analysts accrue Professional Development Units to maintain their certification.

Supplemental Information

There is no supplemental content for this course.

Miscellaneous

The Institute gratefully acknowledges the contribution of Prof. Joseph Hilbe, the original developer and instructor for the course.

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Modeling Count Data
$589 | Enroll Now
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