R square

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R square (R2)

Square of the correlation coefficient. The proportion of the variability in one series that can be explained by the variability of one or more other series a regression model. A measure of the quality of fit. 100% R-square means perfect predictability.

R Square

In statistics, the percentage of a portfolio's performance explainable by the performance of a benchmark index. The R square is measured on a scale of 0 to 100, with a measurement of 100 indicating that the portfolio's performance is entirely determined by the benchmark index, perhaps by containing securities only from that index. A low R square indicates that there is no significant relationship between the portfolio and the index. An R Square is also called the coefficient of determination. See also: Beta.
References in periodicals archive ?
942) between the OTS method and MNB method in all the samples, and both of these two coefficients of determination were higher than 0.
Among these elements, creativity and innovation, structure, content, and repeat have the highest coefficients of determination in order that shows creativity and innovation are better explanations for the increase in banks' clients and the variable of repeat has the minimum impact on banks' clients.
Since the ordinary and adjusted coefficients of determination do not differ significantly, we can conclude that probably nonsignificant terms are not included in the model.
The values showing the strength of the relationship between variables, which are the most important measures of the adequacy of experimental data, namely the coefficients of correlation (R) and coefficients of determination ([R.
The model that best expressed the relationship between the reading of the ClorofiLOG[R] device and the concentration of carotenoids in the leaf tissue was a quadratic model, with coefficients of determination of 80%.
In order to evaluate the adequacy of the regression analyses, coefficients of determination were calculated for each set of loss coefficient data.
In addition, coefficients of determination were calculated for linear regressions applied on the board ranks based on the calculated drying time of individual boards, and board ranks were based on wood properties to calculate how much each property accounted for variations in drying time.
This approach yielded the following empirical correlations for converging flat oval tees and laterals and the corresponding coefficients of determination as calculated using Equation 9:
The correlation coefficients, coefficients of determination and number of cases of the two models are presented in Table 2.
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