# Member Training: Logistic Regression for Count and Proportion Data

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Most of us know that binary logistic regression is appropriate when the outcome variable has two possible outcomes: success and failure. There are two more situations that are also appropriate for binary logistic regression, but they don’t always look like they should be.

Both occur when ultimately, for each individual in the data set, we measure a discrete number of trials (each one of which can result in a success or a failure). Our outcome variable of interest, however, is the total count or proportion of successes on these trials.

Examples include the proportion of questions correct out of 20 on a quiz or the number of days out of 30 that participants in a stop-smoking intervention managed to go without a cigarette.

These are ultimately binomial distributions, but because the outcome variable looks continuous (proportions) or like discrete counts, we try to apply a model that assumes normality or Poisson distributions, respectively.

In this webinar you will learn what these variables are, introduce the relationships between the Poisson, Bernoulli, Binomial, and Normal distributions, and see an example of how to actually set up the data and specify and interpret the logistic model for these kinds of variables.

Note: This training is an exclusive benefit to members of the Statistically Speaking Membership Program and part of the Stat’s Amore Trainings Series. Each Stat’s Amore Training is approximately 90 minutes long. 