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SCENARIO 14-17
Given Below Are Results from the Regression Analysis

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SCENARIO 14-17
Given below are results from the regression analysis where the dependent variable is the number of
weeks a worker is unemployed due to a layoff (Unemploy) and the independent variables are the age
of the worker (Age) and a dummy variable for management position (Manager: 1 = yes, 0 = no).
The results of the regression analysis are given below:   Regression Statistics  Multiple R 0.6391 R Square 0.4085 Adjusted R Square 0.3765 Standard Error 18.8929 Observations 40\begin{array}{l}\hline \ { \text { Regression Statistics } } \\\hline \text { Multiple R } & 0.6391 \\\text { R Square } & 0.4085 \\\text { Adjusted R Square } & 0.3765 \\\text { Standard Error } & 18.8929 \\\text { Observations } & 40 \\\hline\end{array}

 ANOVA \text { ANOVA }
 SCENARIO 14-17 Given below are results from the regression analysis where the dependent variable is the number of weeks a worker is unemployed due to a layoff (Unemploy) and the independent variables are the age of the worker (Age) and a dummy variable for management position (Manager: 1 = yes, 0 = no). The results of the regression analysis are given below:  \begin{array}{l} \hline \ { \text { Regression Statistics } } \\ \hline \text { Multiple R } & 0.6391 \\ \text { R Square } & 0.4085 \\ \text { Adjusted R Square } & 0.3765 \\ \text { Standard Error } & 18.8929 \\ \text { Observations } & 40 \\ \hline \end{array}    \text { ANOVA }       \begin{array} { l r r r r }  \hline & \text { Coefficients } & \text { Standard Error } & { t \text { Stat } } &  { \text { P-value } } \\ \hline \text { Intercept } & - 0.2143 & 11.5796 & - 0.0185 & 0.9853 \\ \text { Age } & 1.4448 & 0.3160 & 4.5717 & 0.0000 \\ \text { Manager } & - 22.5761 & 11.3488 & - 1.9893 & 0.0541 \\ \hline  \end{array}  -Referring to Scenario 14-17, the null hypothesis  H _ { 0 } : \beta _ { 1 } = \beta _ { 2 } = 0  implies that the number of weeks a worker is unemployed due to a layoff is not related to one of the explanatory variables.


 Coefficients  Standard Error t Stat  P-value  Intercept 0.214311.57960.01850.9853 Age 1.44480.31604.57170.0000 Manager 22.576111.34881.98930.0541\begin{array} { l r r r r } \hline & \text { Coefficients } & \text { Standard Error } & { t \text { Stat } } & { \text { P-value } } \\\hline \text { Intercept } & - 0.2143 & 11.5796 & - 0.0185 & 0.9853 \\\text { Age } & 1.4448 & 0.3160 & 4.5717 & 0.0000 \\\text { Manager } & - 22.5761 & 11.3488 & - 1.9893 & 0.0541 \\\hline\end{array}
-Referring to Scenario 14-17, the null hypothesis H0:β1=β2=0H _ { 0 } : \beta _ { 1 } = \beta _ { 2 } = 0 implies that the number of
weeks a worker is unemployed due to a layoff is not related to one of the explanatory variables.


Definitions:

Piecework

A payment method based on the amount of work completed rather than the time taken, frequently used in manufacturing and agriculture.

Variable Pay

A compensation strategy that ties employee pay directly to their performance or achievements, often including bonuses, commissions, and profit-sharing plans.

Competitiveness

The ability of a company or country to maintain and gain market share by producing goods or services more effectively than competitors.

Union Power

The collective strength and influence a group of workers has through their union, often used to negotiate working conditions and wages.

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