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 ?
The adjusted R-squares are disclosed as well as the F-statistic for each industry.
The high adjusted R-squares for the regression equations in each industry indicate that the model has good explanatory power.
Common sampling error across equations leads to OLS coefficient estimates and R-squares that are roughly proportional to the horizon under the null of no predictability.
However the adjusted R-squares are quite low for Models 1 and 3.
For the food sector the models for the overall cost of capital produce high R-squares (64-78 per cent, when unadjusted and 58 to 75 per cent, when adjusted).
We report validated R-squares and average cross-validated R-squares for all three outcomes.
Overall, the ability of the DCG system to explain variance in concurrent utilization was moderate, with R-squares ranging from 18.
This incremental information usefulness can be further quantified by comparing the R-squares of the two regressions.
The adjusted R-squares and variances and skewnesses of the residuals of these regressions were then treated as paired observations.
Adjusted R-squares were calculated to account for any increase in explanatory power due to the larger number of categories in [PM-DRGs.
The option-implied R-squares for individual stocks average 20 percent, which is higher than the regression R-squares on a standard set of marketwide lagged instruments.
While achieving a modest R-square may be consistent with the literature, a reviewer or reader who is not a fan of utilization studies might have no interest in seeing such a study published.