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  2. Coefficient of determination - Wikipedia

    en.wikipedia.org/wiki/Coefficient_of_determination

    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).

  3. Variance inflation factor - Wikipedia

    en.wikipedia.org/wiki/Variance_inflation_factor

    n: greater sample size results in proportionately less variance in the coefficient estimates ^ (): greater variability in a particular covariate leads to proportionately less variance in the corresponding coefficient estimate; The remaining term, 1 / (1 − R j 2) is the VIF. It reflects all other factors that influence the uncertainty in the ...

  4. Nash–Sutcliffe model efficiency coefficient - Wikipedia

    en.wikipedia.org/wiki/Nash–Sutcliffe_model...

    The Nash–Sutcliffe coefficient masks important behaviors that if re-cast can aid in the interpretation of the different sources of model behavior in terms of bias, random, and other components. [11] The alternate Kling–Gupta efficiency is intended to improve upon NSE by incorporating bias and variance terms. [12]

  5. Regression validation - Wikipedia

    en.wikipedia.org/wiki/Regression_validation

    If, for example, the out-of-sample mean squared error, also known as the mean squared prediction error, is substantially higher than the in-sample mean square error, this is a sign of deficiency in the model. A development in medical statistics is the use of out-of-sample cross validation techniques in meta-analysis.

  6. Dummy variable (statistics) - Wikipedia

    en.wikipedia.org/wiki/Dummy_variable_(statistics)

    As with any addition of variables to a model, the addition of dummy variables will increase the within-sample model fit (coefficient of determination), but at a cost of fewer degrees of freedom and loss of generality of the model (out of sample model fit). Too many dummy variables result in a model that does not provide any general conclusions.

  7. Shrinkage (statistics) - Wikipedia

    en.wikipedia.org/wiki/Shrinkage_(statistics)

    This idea is complementary to overfitting and, separately, to the standard adjustment made in the coefficient of determination to compensate for the subjective effects of further sampling, like controlling for the potential of new explanatory terms improving the model by chance: that is, the adjustment formula itself provides "shrinkage." But ...

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  9. Estimating equations - Wikipedia

    en.wikipedia.org/wiki/Estimating_equations

    In statistics, the method of estimating equations is a way of specifying how the parameters of a statistical model should be estimated.This can be thought of as a generalisation of many classical methods—the method of moments, least squares, and maximum likelihood—as well as some recent methods like M-estimators.