Modeling Count Data using Stata

Modeling Count Data using Stata is split into two sections. The theory underlying count models is introduced to students in the first section. The theory is explained in an intuitive manner, with minimal math. The course begins with an introduction to count tables and how to calculate the incidence-rate ratio. The course then progresses to Poisson regression, where students learn how to incorporate continuous, binary, and categorical variables. The concept of overdispersion and the use of negative binomial models to address this issue are then introduced to students. Other count models are discussed, such as truncated models and zero-inflated models.
Students will learn how to apply what they have learned using Stata in the second part of the course. Students will work through a large project to fit Poisson, negative binomial, and zero-inflated models in this section. The tools for comparing these models are also discussed.

KEY FEATURE

Count data are distributed as non-negative integers, are heteroskedastic, skewed to the right, and have a variance that grows with the mean

Count data can only be recorded if events are pre-specified and then recognised and counted during the experiment.

Count data, also known as the number of events per time interval, is a type of discrete data that results from repeated time-to-event observations.

Discrete probability distributions from the Poisson model family can be used to calculate their mean count or piecewise constant event rate.

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Key Topics

    Modeling Count Data using Stata

    • Truncated models
    • Zero-inflated models
    • Predict expected number of outcomes
    • Apply count models using Stata
    • Compare different models
    • Visualise the results
    • Understand count tables
    • Calculate incidence-rate ratios
    • Understand what count models are
    • Identify when to use count models
    • Poisson regression
    • Negative binomial regression
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