A Primer on Simple Linear Regression
Simple linear regression is a foundational technique for modeling the relationship between a predictor variable and a response variable. By fitting a straight line, we can quantify how changes in one variable are associated with changes in another.
The Least Squares Method
The most common approach to estimating the regression line is ordinary least squares (OLS). OLS finds the line that minimizes the sum of squared residuals between the observed data points and the line’s predictions. The slope indicates the strength and direction of the relationship, while the intercept shows the expected value when the predictor is zero.
Understanding simple linear regression is a stepping stone toward more complex modeling techniques, providing crucial intuition about correlation and causation.
