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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 analyst decided to construct a 95% confidence interval for β2 . The confidence interval is from ________ to ________.

 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 analyst decided to construct a 95% confidence interval for β2 .
The confidence interval is from ________ to ________.


Definitions:

Computer Program

A computer program is a set of instructions that a computer follows to perform specific tasks or operations, written in a programming language.

Indemnification

The act of compensating for loss or damage, often seen in contracts where one party agrees to protect the other against certain future claims or liabilities.

Negligently

Acting with a lack of proper care or attention to duties, resulting in harm or damage to others.

Gratuitous Basis

A situation where goods, services, or assets are given without receiving payment or consideration in return.

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