Chapter 7 Simple Linear Regression

The goal of linear regression is to describe the relationship between an independent variable X and a continuous dependent variable \(Y\) as a straight line.

Data for this type of model can arise in two ways;

• Fixed-\(X\): values of \(X\) are preselected by investigator
• Variable-\(X\): have random sample of \((X,Y)\) values

Both Regression and Correlation can be used for two main purposes:

• Descriptive: Draw inferences regarding the relationship
• Predictive: Predict value of \(Y\) for a given value of \(X\)

Simple Linear Regression is an example of a Bivariate analysis since there is only one covariate (explanatory variable) under consideration.

This section uses functions from the gridExtra, sjPlot, broom, performance and ggdist packages to help tidy and visualize results from regression models.