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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 estimate of the unit change in the mean of Y per unit change in  X _ { 1 }  , taking into account the effects of the other variable, is ________.

 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 estimate of the unit change in the mean of Y per unit change in X1X _ { 1 } , taking into account the effects of the other variable, is ________.


Definitions:

Mistreated

Subjected to bad or inappropriate treatment or behavior.

Career Planning

is the ongoing process of setting individual professional goals and exploring paths to achieve them over the course of one's work life.

Random Opportunistic Way

An approach where decisions and actions are taken spontaneously, capitalizing on unforeseen opportunities without a predetermined strategy.

Rationally Planned

The process of systematically organizing tasks or projects through logical and structured planning.

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