Regression in R

Linear regression is one of the most ubiquitous statistical methods. Most statistical techniques can be viewed as either special cases of linear regression (e.g., t-tests, ANOVA) or generalizations of linear regression (e.g., multilevel modeling, SEM, neural networks, GLM, survival analysis). In this workshop, participants will learn how to apply linear regression techniques in R. We will cover (multiple) linear regression, categorical predictor variables, moderation, and diagnostics. Workshop participants will practice what they learn via practical exercises.

Professionals seeking a master-level introduction to linear regression.

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After completing this course, participants can:

Describe how to apply linear regression in R and choose the correct functions with which to implement a given analysis. Write basic R scripts to do the following:

• Run a multiple linear regression model
• Manipulate the fitted model object produced when estimated a linear regression model
• Incorporate categorical predictor variables into linear regression models using an appropriate coding scheme
• Test for moderation using linear regression and conduct a simple slopes analysis
• Check the assumptions of the linear regression model via model diagnostics.