The correct answer is D
To solve this problem, one can assume any value for the total sum of squares. In this case, assume its 1. The regression sum of squares is R2 multiplied by the total sum of squares, which is 0.46. The residual sum of squares is the difference between the total sum of squares and the regression sum of squares, which is 1 ? 0.46 = 0.54. The numerator degrees of freedom is equal to the number of independent variables, which is 4, and the mean regression sum of squares is the regression sum of squares divided by the numerator degrees of freedom, which is 0.46 / 4 = 0.115. The denominator degrees of freedom is the number of observations minus the number of independent variables, minus 1, which is 20 ? 4 ? 1 = 15. The mean squared error is the residual sum of squares divided by the denominator degrees of freedom, which is 0.54 / 15 = 0.036. The F-statistic is the ratio of the mean regression sum of squares to the mean squared error, which is 0.115 / 0.036 = 3.19, which is in between the F-values (with four numerator degrees of freedom and 15 denominator degrees of freedom) of 3.06 for a p-value of 0.05 (calculated using the F-table at 5%) and 3.80 for a p-value of 0.025 (calculated using the F-table at 2.5%).
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