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Regression analysis — Wikipedia (en)

Source: Wikipedia (en)
Original: https://en.wikipedia.org/wiki/Regression_analysis
License (as archived): CC-BY-SA-4.0
Archived: 2026-10-15T15:13:38.910Z
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Regression analysis — Wikipedia (en) — Wikipedia (en), https://en.wikipedia.org/wiki/Regression_analysis (CC-BY-SA-4.0); archived copy: https://metraipolis.com/documento/dc_6b5d928fa057 (Metraipolis archive dc_6b5d928fa057, sha256 bebfd3a033efc5fc4fe92f74bc3eb97e9111835596a0ed2cc064232469225922)

BibTeX

@misc{dc6b5d928fa057,
  title = {Regression analysis — Wikipedia (en)},
  howpublished = {Wikipedia (en)},
  url = {https://en.wikipedia.org/wiki/Regression_analysis},
  note = {License CC-BY-SA-4.0; archived copy https://metraipolis.com/documento/dc_6b5d928fa057; sha256 bebfd3a033efc5fc4fe92f74bc3eb97e9111835596a0ed2cc064232469225922},
  year = {2026}
}

Archived text

Reproduced under CC-BY-SA-4.0. Attribution: Wikipedia (en), https://en.wikipedia.org/wiki/Regression_analysis. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

In statistical modeling, regression analysis is a statistical method for estimating the relationship between a dependent variable (often called the outcome or response variable, or a label in machine learning parlance) and one or more independent variables (often called regressors, predictors, covariates, explanatory variables or features). The most common form of regression analysis is linear regression, in which one finds the line or hyperplane that most closely fits the data according to a specific mathematical criterion. For example, the method of ordinary least squares computes the unique line (or hyperplane) that minimizes the sum of squared differences between the true data and that line (or hyperplane). For specific mathematical reasons (see linear regression), this allows the researcher to estimate the conditional expectation (or population average value) of the dependent variable when the independent variables take on a given set of values. Less common forms of regression use slightly different procedures to estimate alternative location parameters (e.g., quantile regression or Necessary Condition Analysis) or estimate the conditional expectation across a broader collection of non-linear models (e.g., nonparametric regression). Linear regression can model curved relationships. The "linear" refers to linearity in the parameters: by including transformed predictors, for example using polynomials, the fitted line or hyperplane can take a variety of non-linear shapes. Linear regression can also model interactions between variables by including products of predictors, allowing the effect of one variable to depend on the value of another. Regression analysis is primarily used for two conceptually distinct purposes. First, regression analysis is widely used for prediction and forecasting, where its use has substantial overlap with the field of machine learning. Second, in some situations regression analysis can be used to infer causal relationships between the independent and dependent variables. Importantly, regressions by themselves only reveal relationships between a dependent variable and a collection of independent variables in a fixed dataset. To use regressions for prediction or to infer causal relationships, respectively, a researcher must carefully justify why existing relationships have predictive power for a new context or why a relationship between two variables has a causal interpretation. The latter is especially important when researchers hope to estimate causal relationships using observational data.

Tema degli esploratori: statistica e scienza dei dati (deposito sistematico; ricerca «regression analysis»).
Fonte: Wikipedia in inglese, voce «Regression analysis» — https://en.wikipedia.org/wiki/Regression_analysis
Wikidata Q208042 (dati Wikidata in CC0 1.0) — https://www.wikidata.org/wiki/Q208042
Autori: i contributori di Wikipedia (cronologia: https://en.wikipedia.org/wiki/Regression_analysis?action=history).
Licenza: Creative Commons Attribuzione-Condividi allo stesso modo 4.0 (CC BY-SA 4.0) — https://creativecommons.org/licenses/by-sa/4.0/. Il riuso richiede attribuzione e stessa licenza.
Estratto dell'introduzione non modificato nel contenuto; revisione 1378914971, consultata il 2026-10-15T14:17:49.537Z tramite l'API ufficiale di Wikipedia. Impronta SHA-256 dell'estratto: a81fa5558e16a8e56c43b4f35bde1d6803ea546f71ec48c18311a47fa4e0faee