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- Q17014030 subject Q7035969.
- Q17014030 subject Q8250031.
- Q17014030 subject Q8646998.
- Q17014030 abstract "In statistics, a fixed effects model is a statistical model that represents the observed quantities in terms of explanatory variables that are treated as if the quantities were non-random. This is in contrast to random effects models and mixed models in which either all or some of the explanatory variables are treated as if they arise from random causes. Contrast this to the biostatistics definitions, as biostatisticians use "fixed" and "random" effects to respectively refer to the population-average and subject-specific effects (and where the latter are generally assumed to be unknown, latent variables). Often the same structure of model, which is usually a linear regression model, can be treated as any of the three types depending on the analyst's viewpoint, although there may be a natural choice in any given situation.In panel data analysis, the term fixed effects estimator (also known as the within estimator) is used to refer to an estimator for the coefficients in the regression model. If we assume fixed effects, we impose time independent effects for each entity that are possibly correlated with the regressors.".
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- Q17014030 wikiPageWikiLink Q7035969.
- Q17014030 wikiPageWikiLink Q8250031.
- Q17014030 wikiPageWikiLink Q857354.
- Q17014030 wikiPageWikiLink Q8646998.
- Q17014030 comment "In statistics, a fixed effects model is a statistical model that represents the observed quantities in terms of explanatory variables that are treated as if the quantities were non-random. This is in contrast to random effects models and mixed models in which either all or some of the explanatory variables are treated as if they arise from random causes.".
- Q17014030 label "Fixed effects model".