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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 Kling–Gupta efficiency (KGE) is a goodness-of-fit indicator widely used in the hydrologic sciences for comparing simulations to observations. It was created by hydrologic scientists Harald Kling and Hoshin Vijai Gupta. [1]
One measure of goodness of fit is the coefficient of determination, often denoted, R 2.In ordinary least squares with an intercept, it ranges between 0 and 1. However, an R 2 close to 1 does not guarantee that the model fits the data well.
Then, calculate the VIF factor for ^ with the following formula : = where R 2 i is the coefficient of determination of the regression equation in step one, with on the left hand side, and all other predictor variables (all the other X variables) on the right hand side.
The NSE coefficient is sensitive to extreme values and might yield sub-optimal results when the dataset contains large outliers. To address this a modified version of NSE has been suggested where the sums of squares in the numerator and denominator of NSE are raised to 1 instead of 2 and the resulting modified NSE values compared to the ...
In statistics, canonical analysis (from Ancient Greek: κανων bar, measuring rod, ruler) belongs to the family of regression methods for data analysis. Regression analysis quantifies a relationship between a predictor variable and a criterion variable by the coefficient of correlation r, coefficient of determination r 2, and the standard regression coefficient β.
In statistics, the Jonckheere trend test [1] (sometimes called the Jonckheere–Terpstra [2] test) is a test for an ordered alternative hypothesis within an independent samples (between-participants) design.
The coefficient of multiple correlation is known as the square root of the coefficient of determination, but under the particular assumptions that an intercept is included and that the best possible linear predictors are used, whereas the coefficient of determination is defined for more general cases, including those of nonlinear prediction and those in which the predicted values have not been ...
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