TABLE 13-9
It is believed that, the average numbers of hours spent studying per day (HOURS) during undergraduate education should have a positive linear relationship with the starting salary (SALARY, measured in thousands of dollars per month) after graduation. Given below is the Excel output from regressing starting salary on number of hours spent studying per day for a sample of 51 students. NOTE: Some of the numbers in the output are purposely erased.
Regression Stedistics

\begin{array} { l l } \text { Multiple R } & 0.8857 \\ \text { RSquare } & 0.7845 \\ \text { Adjusted R Square } & 0.7801 \\ \text { Standard Error } & 1.3704 \end{array}

Observations 51

ANOVA

\begin{array}{lccrcc}\hline & d f & S S &{M S} & F & \text { Significance } F \\\hline \text { Regression } & 1 & 335.0472 & 335.0473 & 178.3859 \\\text { Residual } & & & 1.8782 & \\\text { Total } & 50 & 427.0798 & &\end{array}

\begin{array}{lrccccr} & \text { Coeffcients } & \text { Standard Error } &t \text { Stat } & p-{\text {-value }} & \text { Lower 95\% } & \text { Upper 95\% } \\\hline \text { Intercept } & -1.8940 & 0.4018 & -4.7134 & 2.051 \mathrm{E}-05 & -2.7015 & -1.0865 \\\text { Hours } & 0.9795 & 0.0733 & 13.3561 & 5.944 \mathrm{E}-18 & 0.8321 & 1.1269 \\\hline\end{array}

-Referring to Table 13-9, the p-value of the measured F-test statistic to test whether HOURS affects SALARY is
A) 2.051E-05.
B) (2.051E-05)/2.
C) 5.944E-18.
D) (5.944E-18)/2.

The Y-Intercept (B0) Represents the

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The Y-Intercept (B₀) Represents the

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