R² is a measure of the strength of the relationship between a regression model and its independent variable(s), recalling from your elementary statistics courses that we use independent variables to project, or forecast, dependent variables. For example, in a business setting, we may use social media advertising and subsequent sales to project gross revenue. Or, in a manufacturing setting, we may use demand to project product manufacturing. And from an investment perspective, we may use the market and a sub-sector to project equity price. We can include several variables in the mix, or only a few. However, developing the model is only part of the equation, pun intended. To appreciate how well the model projects, or how well the model is fitted to the independent variables, i.e., predictor variables, we need to understand R².