regression analysis

Regression analysis is a statistical technique used to estimate one variable based on another. It involves identifying the relationship between a dependent variable (whose value is determined by other variables) and one or more independent variables.123

The goal is to find a line or curve that best represents the trend in a dataset. This is often achieved using mathematical and statistical techniques, such as regression coefficients, curves, equations, or lines, applied to empirical data. In simple linear regression, the model is represented as (y = \beta_0 + \beta_1x + \epsilon), where (y) is the dependent variable, (x) is the independent variable, (\beta_0) and (\beta_1) are model parameters, and (\epsilon) is an error term. Multiple regression extends this to include several independent variables. If the model is satisfactory, it can be used to predict the dependent variable's value based on the independent variables.412