Regression models, such as linear, logistic, time to event, and mixed models, measure the strength of the association between the dependent variable and the independent variables.
These models allow us to explain relationships and predict values of the dependent variable.
But for observational data, these models don’t allow us to conclude the independent variables have a causal relationship with the dependent variable.
But others do.
In this Stats Amore presentation, we will review five models that do allow us to explore causal relationships.
By the end of the presentation, you will understand:
- what type of data is used for each model
- when and why each specific model is used
- the logic behind each model
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
Jeff Meyer is a statistical consultant and the Stata expert at The Analysis Factor. He teaches workshops and provides Stata examples for a number of our workshops, including Intro to Stata, Missing Data, and Repeated Measures.
Jeff has an MBA from the Thunderbird School of Global Management and an MPA with a focus on policy from NYU Wagner School of Public Service.
Just head over and sign up for Statistically Speaking. You'll get access to this training webinar, 130+ other stats trainings, a pathway to work through the trainings that you need — plus the expert guidance you need to build statistical skill with live Q&A sessions and an ask-a-mentor forum.
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