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SCENARIO 13-17
Given Below Are Results from the Regression Analysis

question 241

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SCENARIO 13-17
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 ANOVA  df  SS  MS  F  Significance F Regression 29119.08974559.544812.77400.0000 Residual 3713206.8103356.9408 Total 3922325.9 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}\begin{array} { l r } \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 r } \hline &{ \text { df } } & { \text { SS } } & { \text { MS } } &{ \text { F } } & { \text { Significance } F } \\\hline \text { Regression } & 2 & 9119.0897 & 4559.5448 & 12.7740 & 0.0000 \\\text { Residual } & 37 & 13206.8103 & 356.9408 & \\\text { Total } & 39 & 22325.9 & & \\\hline\end{array}\\\\\begin{array} { l r r r r } \hline & \text { Coefficients } & \text { Standard Error } & { t \text { Stat } } & { P \text {-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}\end{array}
-Referring to SCENARIO 13-17, which of the following is the correct null hypothesis to test whether age has any effect on the number of weeks a worker is unemployed due to a layoff while holding constant the effect of the other independent variable?


Definitions:

Materials Quantity Variance

The difference between the actual quantity of materials used in production and the expected quantity, multiplied by the standard cost per unit.

Variable Overhead Efficiency Variance

The difference between the actual variable overhead incurred and the standard cost allocated for the actual production achieved.

Labor Rate Variance

The difference between the actual cost of direct labor and the expected (or budgeted) cost, based on standard rates and actual hours worked.

Materials Price Variance

The difference between the actual cost of materials purchased and the expected (or standard) cost, used to assess cost management performance in procurement.

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