 Simple linear regression

Simple linear regression uses one predictor and an intercept, $Y_i=\alpha +\beta x_i+{\epsilon }_i$.

Simple linear regression is a statistical method used to model the relationship between two continuous variables by fitting a linear equation to the observed data. It assumes that there is a linear relationship between the independent variable (predictor) and the dependent variable (response). ### Key Components of Simple Linear Regression: 1. **Independent Variable (X)**: This is the variable that you use to predict the value of the dependent variable. It is also known as the predictor, feature, or explanatory variable.