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Instruction 12  Regression statistics \text { Regression statistics } Note: 2051E-05 = 2

question 22

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Instruction 12.8
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 Microsoft Excel output for predicting starting salary (Y) using number of hours spent studying per day (X) for a sample of 51 students. NOTE: Only partial output is shown.
 Regression statistics \text { Regression statistics }
 MultipleR 0.8857 R Square 0.7845 Adjusted R  Square 0.7801 Standard Error 1.3704 Observations 51\begin{array}{|l|l|}\hline\text { MultipleR } & 0.8857 \\\hline \text { R Square } & 0.7845 \\\hline \begin{array}{l}\text { Adjusted R } \\\text { Square }\end{array} & 0.7801 \\\hline \text { Standard Error } & 1.3704 \\\hline \text { Observations } & 51\\\hline\end{array}

 ANOVA df55 MS F Significance F Regression 1335.0472335.0473178.3859 Residual 1.8782 Total 50427.0798\begin{array}{l}\text { ANOVA }\\\begin{array}{|l|l|l|l|l|l|l|}\hline & d f & 55 & \text { MS } & F & \begin{array}{l}\text { Significance } \\F\end{array} & \\\hline \text { Regression } & 1 & 335.0472 & 335.0473 & 178.3859 & & \\\hline \text { Residual } & & & 1.8782 & & & \\\hline \text { Total } & 50 & 427.0798 & & & & \\\hline\end{array}\end{array}

 Coefficients  Standard  Error  tStat  p-value  Lower 95%  Upper 95%  Intercept 1.89400.40184.71342.051E052.70151.0865 Hours 0.97950.073313.35615.944E180.83211.1269\begin{array}{|l|l|l|l|l|l|l|}\hline & \text { Coefficients } & \begin{array}{l}\text { Standard } \\\text { Error }\end{array} & \text { tStat } & \text { p-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 \\\hline \text { Hours } & 0.9795 & 0.0733 & 13.3561 & 5.944 \mathrm{E}-18 & 0.8321 & 1.1269 \\\hline\end{array} Note: 2.051E-05 = 2.051 * 10S1-0.5 and 5.944E-18 = 5.944 * 10S1-18.
-Referring to Instruction 12.8,the error sum of squares (SSE) of the above regression is


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