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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:

Intermediaries

Entities that facilitate transactions or interactions between two or more parties in a supply chain, often involved in the distribution, wholesaling, and retailing of goods.

Retailers

Businesses or individuals that sell goods directly to consumers, acting as the final link in the supply chain from manufacturers to consumers.

Retailer

A business that sells products directly to consumers, offering products from one or several suppliers for sale to the end-user.

Marketing Channel

A system of intermediaries that helps to move products from the manufacturer to the consumer.

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