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TABLE 13-9
It Is Believed That, the Average Numbers of Hours

question 65

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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 Statistics  Multiple R 0.8857 R Square 0.7845 Adjusted R Square 0.7801 Standard Error 1.3704 Observations 51\begin{array}{l}\text { Regression Statistics }\\\begin{array} { l c } \hline \text { Multiple R } & 0.8857 \\\text { R Square } & 0.7845 \\\text { Adjusted R Square } & 0.7801 \\\text { Standard Error } & 1.3704 \\\text { Observations } & 51 \\\hline\end{array}\end{array}
 ANOVA \text { ANOVA }
 df  SS  MS  Significance F  Regression 1335.0472335.0473178.3859 Residual 1.8782 Total 50427.0798\begin{array}{llcc}\hline&\text { df }&\text { SS } &\text { MS }&\text {F }&\text { Significance F } \\\text { Regression } & 1 & 335.0472 & 335.0473 & 178.3859 \\\text { Residual } & & & 1.8782 & \\\text { Total } & 50 & 427.0798 & &\end{array}  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.   \begin{array}{l} \text { Regression Statistics }\\ \begin{array} { l c }  \hline \text { Multiple R } & 0.8857 \\ \text { R Square } & 0.7845 \\ \text { Adjusted R Square } & 0.7801 \\ \text { Standard Error } & 1.3704 \\ \text { Observations } & 51 \\ \hline \end{array} \end{array}    \text { ANOVA }   \begin{array}{llcc} \hline&\text { df }&\text { SS } &\text { MS }&\text {F }&\text { Significance F } \\ \text { Regression } & 1 & 335.0472 & 335.0473 & 178.3859 \\ \text { Residual } & & & 1.8782 & \\ \text { Total } & 50 & 427.0798 & & \end{array}    -Referring to Table 13-9, the 90% confidence interval for the average change in SALARY (in thousands of dollars)  as a result of spending an extra hour per day studying is A)  wider than [-2.70159, -1.08654]. B)  wider than [0.8321927, 1.12697]. C)  narrower than [0.8321927, 1.12697]. D)  narrower than [-2.70159, -1.08654].
-Referring to Table 13-9, the 90% confidence interval for the average change in SALARY (in thousands of dollars) as a result of spending an extra hour per day studying is


Definitions:

Market Efficiency

A financial theory stating that asset prices fully reflect all available information, making it impossible to consistently achieve higher returns.

Efficient Capital Market

Market in which security prices reflect available information.

Security Prices

The cost or price of financial instruments such as stocks, bonds, and derivatives, determined by market supply and demand.

Compound Return

The process by which an investment grows over time as earnings from both the principal and the accumulated earnings from preceding periods.

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