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.
ggdistpackages to help tidy and visualize results from regression models.