régression

Régression is a statistical method used to model the relationship between a dependent variable and one or more independent variables. The goal is to understand how changes in the independent variables affect the dependent variable. This modeling can be used for prediction, forecasting, or inferring causal relationships.

There are several types of regression analysis. Linear regression, one of the most common, assumes a linear relationship between the variables. In simple linear regression, there is only one independent variable, while multiple linear regression involves two or more. Non-linear regression models relationships that are not linear, using different functional forms. Logistic regression is used when the dependent variable is categorical, typically binary.

The process of regression involves estimating parameters that best fit the observed data. This is often done using techniques like ordinary least squares, which minimizes the sum of squared differences between the observed and predicted values of the dependent variable. Once a regression model is built, its performance is evaluated using various metrics, such as R-squared, which indicates the proportion of variance in the dependent variable explained by the independent variables.

Régression analysis is widely applied in various fields, including economics, finance, medicine, and social sciences, to analyze trends, make predictions, and test hypotheses. It is a fundamental tool in data analysis and statistical modeling.