Title: Using JMP to Performa Bayesian Calculations Background: Accreditation Council for Graduate Medical Education states that “Residents must have sufficient training in…endotracheal intubation” and that achieving 80% likelihood to successfully intubate the next patient is a good rule of thumb. Methods: To assess the likelihood that a resident has achieved this level of competency during residency training, JMP 9.0 (Cary, NC) was used to create a Bayesian program to determine on the basis of a provider’s prior intubation experiences the likelihood that they behaved like a novice (probability to intubate = 30%) or a competent provider (probability > 80%). Validation of the approach was achieved by comparing the prior Bayesian likelihoods of intubation competency to subsequent intubation outcomes. Results: An 80% likelihood that the provider was competent demonstrated an 80% or higher intubation success rate on subsequent intubation attempts. Unfortunately, most of the residents had not attained this level of competency during the period of study. Conclusion: A Bayesian approach to the assessment of intubation competency can accurately predict the outcome of subsequent intubation events. Future studies will be required to determine if new training techniques (video assisted) can significantly improve the learning curve.