Coefficient of determination

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Coefficient of determination

A measure of the goodness of fit of the relationship between the dependent and independent variables in a regression analysis; for instance, the percentage of variation in the return of an asset explained by the market portfolio return. Also known as R-square.

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.
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Nonetheless, the value of R-square indicated in Table 2 is quite low (less than 30%) which suggests that firm value is not influenced to any great extent by the explanatory variable in the model (ERM implementation).
Initial results yielded inconsistent R-square values and statistically insignificant P-values.
it] Table 3: Cross-section fixed effect panel single regression results on RIC of MVA and accounting measures with SR (H1) T statistic R-square Adj.
However, a number of pseudo R-square measures have been developed that share the desirable characteristic of the OLS [R.
Further, the regression R-square should decrease as Timeliness increases or decreases from average.
R-square values for the other orders were calculated with Coats-Redfern method and the results are given in Table 1 and shown in Fig.
When the susceptibilities of DGM at high resolution and iron content (mg/100g) were correlated, a positive correlation was found with R-square (R2=0.
The best three-variable models were formed by adding another variable one by one from the remaining variables, and the variables that yielded the greatest increase in the adjusted R-square value besides keeping the variance inflation factor (ViF) below the threshold value of 3.
Although removal of these variables did reduce the overall multiple R-square for the final model slightly (i.
Finally, we compute statistics such as R-square based on the monthly estimates computed above.