Question

What does the R² tell us about the reliability of the results? Does the model explain a large fraction of the variance in LVRs across the sample?

Source df MS Number of obs - 1,716 17.27 0.0000 0.0661 0.0623 1652 F (7, 1708) Model 3.2992079 7 .471315415 ProbE Residual 46.6103775 1,708 .027289448 R-squared Adj R-squared = Total 49.9095854 1,715 .029101799 Root MSE lvr Coef Std. 2rr. [95% Conf. Interval] -.007698 0000835 0001327 -.0163009 0428063 026664 0045128 6569055 0016265 0000343 0000678 0098569 0110465 0089636 0007639 0236518 -4.73 0.000 2.43 0.015 1.96 0.050 -1.65 0.098 3.88 0.000 2.97 0.003 5.91 0.000 27.77 0.000 totinc -.0108881 -.00 0000162 -2.05e-07 -.0356337 0211402 0090832 0030144 6105159 0001508 0002656 0030318 0644723 0442448 0060112 703295 Square liq sch gift fixadj term vr cons

Answer 1

Given the information above ,

R^2 = 0.0661

6.61% Variabilty of response variable explained by the explanatory variables

No, the model is not explain a large fraction of the variance in LVRs across the sample .