|Where:||Join us from anywhere!|
|Cost:||Free—but registrations are limited|
Are you unsure which predictors to include in your model? Rather than choosing one model, aggregate results across all candidate models to account for model uncertainty with Bayesian model averaging (BMA). Which predictors are important given the observed data? Which models are more plausible? How do predictors relate to each other across different models? BMA can answer these questions and many more.
Stata 18 introduced the bma suite of commands to perform BMA in linear regression models. In this webinar, you will learn how to explore influential models, make inferences, and obtain better predictions with BMA. We will demonstrate the utility of BMA for any researcher—Bayesian, frequentist, and everyone in between! No prior knowledge of the Bayesian framework is required.
The webinar is free, but you must register to attend. Registrations are limited so register soon.
We will send you an email prior to the start with instructions on how to access the webinar.
Meghan Cain is the Assistant Director, Educational Services at StataCorp LLC. She earned her PhD in quantitative psychology from the University of Notre Dame, where her research focused on structural equation modeling, multilevel modeling, and Bayesian statistics. At Stata, she develops and presents training on these and other topics. She also conducts webinars, works with developers to produce Stata documentation, and contributes to Stata blogs.
Classroom & web training
Teaching with Stata
Statalist: The Stata Forum
Last updated: 16 November 2022
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