This course provides a bridge between undergraduate training and modern Bayesian methods for data analysis, which is becoming the accepted research standard. Knowledge of algebra and basic calculus is a prerequisite.
- Introduction: Credibility, Models, and Parameters
- The R Programming Language
- What Is This Stuff Called Probability?
- Bayes’ Rule
- Inferring a Binomial Probability via Exact Mathematical Analysis
- Markov Chain Monte Carlo
- Hierarchical Models
- Model Comparison and Hierarchical Modeling
- Null Hypothesis Significance Testing
- Bayesian Approaches to Testing a Point (“Null”) Hypothesis
- Goals, Power, and Sample Size
- Overview of the Generalized Linear Model
- Metric-Predicted Variable on One or Two Groups