regres

Regres is a term encountered in several languages to denote regression, particularly in statistics. In this context, regres refers to methods for describing the relationship between a dependent variable and one or more independent variables. The goal is to predict or understand how the outcome changes as predictors vary.

Common forms include simple linear regression, multiple regression, and logistic regression for binary outcomes. Estimation methods include ordinary least squares for linear models, maximum likelihood for generalized models, and Bayesian approaches. Model selection and regularization (such as ridge and LASSO) help prevent overfitting.

Diagnostics involve examining residuals, checking assumptions (linearity, homoscedasticity, independence), and using metrics like R-squared or information criteria. Causal interpretation requires caution, as regression identifies associations rather than proven cause-and-effect relationships. Extrapolation beyond the observed data can be unreliable.

Historically, the concept traces back to Francis Galton’s observations of regression toward the mean in the 19th century and evolved into a core tool of statistics and econometrics. Regres is widely used across fields, including economics, biology, engineering, social sciences, and public health, for forecasting, trend analysis, and inferential modeling.

In contexts outside statistics, regres may appear as a root in discourse or as a proper noun in non-English languages. The exact meaning depends on language and discipline.