This module will introduce linear regression as a tool for studying relationships between continuous outcomes and continuous, binary, and categorical predictors. Using linear regression as the foundation, we will explore other regression methods, including logistic regression for the analysis of binary outcomes. Specific topics discussed are: linear regression; regression diagnostics; ANOVA; multiple comparisons; logistic regression; generalized linear models. Participants will have the opportunity for hands-on experience, using R. This module is designed as a foundation for the quantitative genetics and association mapping modules. It assumes the material in Module 1 and will cover the basic commands in R. Suggested pairing: Modules 8 and 11. 

Learning Objectives: After attending this module, participants will be able to:

1. Implement simple and multiple linear regression models in R in order to investigate the association between one or more predictor variables and a continuous outcome.
2. Interpret the results of a linear regression model in the context of a scientific question of interest.
3. Evaluate the results of graphical and numerical regression diagnostics and assess the potential impact of violations of linear regression modeling assumptions on analysis results.
4. Compare and contrast alternative measures for the strength of association between exposure variables and a binary outcome.
5. Implement multiple logistic regression models in R in order to investigate the association between one or more predictor variables and a binary outcome.
6. Interpret the results of a logistic regression model in the context of a scientific question of interest.