👉 Regression math is a statistical method used to model the relationship between a dependent variable (the outcome we want to predict) and one or more independent variables (features or predictors). It involves finding a linear or non-linear equation that best fits the observed data, minimizing the difference between predicted and actual values. The most common form is simple linear regression, where the relationship is modeled as a straight line (y = β0 + β1x + ε), with β0 being the intercept, β1 the slope, and ε the error term. For multiple regression, this extends to y = β0 + β1x1 + β2x2 +... + βnxn + ε, where each xi represents an independent variable and n is the number of predictors. The goal is to estimate coefficients (β0, β1,..., βn) that minimize the sum of squared residuals (differences between observed and predicted values), often using methods like Ordinary Least Squares (OLS). This allows us to make predictions and understand how changes in independent variables affect the dependent variable.
