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Regression analysis is a statistical technique for modeling and quantifying relationships between a dependent target variable and one or more independent predictors. It is used for description, prediction and causal estimation. Key aspects include model assumptions, goodness-of-fit metrics, regularization and careful validation to avoid bias.
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Regression analysis models the relationship between an outcome and explanatory variables for description, estimation, or prediction.
It grew from least-squares fitting and nineteenth-century statistics. Modern regression includes linear and nonlinear models for many data types.
A model places a line or curve through observations and estimates how the outcome changes with predictors. Coefficients, uncertainty, and fit measures belong together. Association alone does not establish causation; outliers, confounding, and bias can dominate results.
The variable to explain or predict.
An explanatory variable whose relationship with the outcome is modeled.
Intervals and error measures show how dependable estimates are.
Regression supports forecasting, experiments, and causal hypotheses. Assumptions, data quality, confounding, and overfitting determine whether a result is trustworthy.
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