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.
About the Instructor
Karen Grace-Martin helps statistics practitioners gain an intuitive understanding of how statistics is applied to real data in research studies.
She has guided and trained researchers through their statistical analysis for over 15 years as a statistical consultant at Cornell University and through The Analysis Factor. She has master’s degrees in both applied statistics and social psychology and is an expert in SPSS and SAS.
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