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In statistics, the variance inflation factor (VIF) is the ratio of the variance of a parameter estimate when fitting a full model that includes other parameters to the variance of the parameter estimate if the model is fit with only the parameter on its own. [1]
Variance inflation factor, a measure of collinearity in statistical regression models; Visual information fidelity, measure for image quality assessment; Value of in-force, a life insurance term "Virtual Interface", a networking term; Viral infectivity factor of retroviruses, specifically used in the context of HIV "Vector Unit InterFace" on ...
Variance inflation factors are often misused as criteria in stepwise regression (i.e. for variable inclusion/exclusion), a use that "lacks any logical basis but also is fundamentally misleading as a rule-of-thumb".
Ordinary least squares regression of Okun's law.Since the regression line does not miss any of the points by very much, the R 2 of the regression is relatively high.. In statistics, the coefficient of determination, denoted R 2 or r 2 and pronounced "R squared", is the proportion of the variation in the dependent variable that is predictable from the independent variable(s).
The design effect is a positive real number that indicates an inflation (>), or deflation (<) in the variance of an estimator for some parameter, that is due to the study not using SRS (with =, when the variances are identical).
About Wikipedia; Contact us; Contribute Help; ... Fano factor; Full width at half maximum; G. ... Variance inflation factor; Variance of the mean and predicted responses;
Firstly, if the true population mean is unknown, then the sample variance (which uses the sample mean in place of the true mean) is a biased estimator: it underestimates the variance by a factor of (n − 1) / n; correcting this factor, resulting in the sum of squared deviations about the sample mean divided by n-1 instead of n, is called ...
where , , > and (+ ) + <, which ensures the non-negativity and stationarity of the variance process. For stock returns, parameter θ {\displaystyle ~\theta } is usually estimated to be positive; in this case, it reflects a phenomenon commonly referred to as the "leverage effect", signifying that negative returns increase future volatility by a ...