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SCENARIO 14-8 a Financial Analyst Wanted to Examine the Relationship $1,000 \$ 1,000

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SCENARIO 14-8 A financial analyst wanted to examine the relationship between salary (in $1,000 \$ 1,000 ) and 2 variables: age (X1=Age) \left(X_{1}=\mathrm{Age}\right) and experience in the field (X2= \left(X_{2}=\right. Exper). He took a sample of 20 employees and obtained the following Microsoft Excel output:

 Regression Statistics  Multiple R 0.8535 R Square 0.7284 Adjusted R Square 0.6964 Standard Error 10.5630 Observations 20\begin{array}{lr}\hline {\text { Regression Statistics }} \\\hline \text { Multiple R } & 0.8535 \\\text { R Square } & 0.7284 \\\text { Adjusted R Square } & 0.6964 \\\text { Standard Error } & 10.5630 \\\text { Observations } & 20 \\\hline\end{array}

 ANOVA \text { ANOVA }
 SCENARIO 14-8 A financial analyst wanted to examine the relationship between salary (in   \$ 1,000   ) and 2 variables: age   \left(X_{1}=\mathrm{Age}\right)   and experience in the field   \left(X_{2}=\right.   Exper). He took a sample of 20 employees and obtained the following Microsoft Excel output:   \begin{array}{lr} \hline {\text { Regression Statistics }} \\ \hline \text { Multiple R } & 0.8535 \\ \text { R Square } & 0.7284 \\ \text { Adjusted R Square } & 0.6964 \\ \text { Standard Error } & 10.5630 \\ \text { Observations } & 20 \\ \hline \end{array}    \text { ANOVA }      \begin{array}{lrrrrrr}  & \text { Coefficients } & \text { Standard Error } & {\text { t Stat }} & \text { P-value } & \text { Lower 95\% } &{\text { O5\% }} \\ \hline \text { Intercept } & 1.5740 & 9.2723 & 0.1698 & 0.8672 & -17.9888 & 21.1368 \\ \text { Age } & 1.3045 & 0.1956 & 6.6678 & 0.0000 & 0.8917 & 1.7173 \\ \text { Exper } & -0.1478 & 0.1944 & -0.7604 & 0.4574 & -0.5580 & 0.2624 \\ \hline \end{array}   Also the sum of squares due to the regression for the model that includes only Age is 5022.0654 while the sum of squares due to the regression for the model that includes only Exper is 125.9848. -Referring to Scenario 14-8, the value of the partial F test statistic is ____ for  H _ { 0 }  : Variable  X _ { 1 }  does not significantly improve the model after variable  X _ { 2 }  has been included   H _ { 1 }  : Variable  X _ { 1 }  significantly improves the model after variable  X _ { 2 }  has been included

 Coefficients  Standard Error  t Stat  P-value  Lower 95%  O5%  Intercept 1.57409.27230.16980.867217.988821.1368 Age 1.30450.19566.66780.00000.89171.7173 Exper 0.14780.19440.76040.45740.55800.2624\begin{array}{lrrrrrr} & \text { Coefficients } & \text { Standard Error } & {\text { t Stat }} & \text { P-value } & \text { Lower 95\% } &{\text { O5\% }} \\\hline \text { Intercept } & 1.5740 & 9.2723 & 0.1698 & 0.8672 & -17.9888 & 21.1368 \\\text { Age } & 1.3045 & 0.1956 & 6.6678 & 0.0000 & 0.8917 & 1.7173 \\\text { Exper } & -0.1478 & 0.1944 & -0.7604 & 0.4574 & -0.5580 & 0.2624 \\\hline\end{array}
Also the sum of squares due to the regression for the model that includes only Age is 5022.0654 while the
sum of squares due to the regression for the model that includes only Exper is 125.9848.
-Referring to Scenario 14-8, the value of the partial F test statistic is ____ for H0H _ { 0 } : Variable X1X _ { 1 } does not significantly improve the model after variable X2X _ { 2 } has been included
H1H _ { 1 } : Variable X1X _ { 1 } significantly improves the model after variable X2X _ { 2 } has been included


Definitions:

Hourly Worker

An employee whose pay is based on the number of hours worked rather than receiving a fixed salary.

Shop Foreman

A supervisory role within a manufacturing or industrial site, overseeing the work of employees and ensuring the efficiency and quality of production.

Work-In-Process Inventory

Goods that are partially completed in the manufacturing process but not yet ready for sale.

Cost Of Goods Sold

In a perpetual inventory system, an account that records the cost of merchandise inventory used to make the sale. Also, it is the total cost of the goods which were sold to customers.

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