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

question 168

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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:

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

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

$\begin{array}{lrrrrrr} & \text { Coefficients } & \text { Standard Error } & {\text { t Stat }} & \text { P-value } & \text { Lower 95\% } &{\text { O5\% }} \\\hline \text { Intercept } & 1.5740 & 9.2723 & 0.1698 & 0.8672 & -17.9888 & 21.1368 \\\text { Age } & 1.3045 & 0.1956 & 6.6678 & 0.0000 & 0.8917 & 1.7173 \\\text { Exper } & -0.1478 & 0.1944 & -0.7604 & 0.4574 & -0.5580 & 0.2624 \\\hline\end{array}$
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

Definitions:

Hmos

Health Maintenance Organizations; a type of health insurance plan that provides managed care for a fixed fee, emphasizing prevention and efficiency in healthcare service.

Health Coverage

Refers to the extent of financial protection and access to health services a person has through insurance or other means.

Employees

Individuals who are hired by a business or an individual to perform work in exchange for compensation.

High Tech Cures

Refers to advanced medical treatments and diagnostics that utilize cutting-edge technology and research.

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

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