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SCENARIO 17-10 Given Below Are Results from the Regression Analysis 1=1 =

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SCENARIO 17-10 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), the number of years of education received (Edu), the number of years at the previous job (Job Yr), a dummy variable for marital status (Married: 1=1 = married, 0=0 = otherwise), a dummy variable for head of household (Head: 1=1 = yes, 0=0 = no) and a dummy variable for management position (Manager: 1=1 = yes, 0=0 = no). We shall call this Model 1. The coefficient of partial determination ( Ry2R _ { \mathrm { y } } ^ { 2 } (All raiables excopt jj ) ) of each of the 6 predictors are, respectively, 0.28070.2807 , 0.0386,0.0317,0.0141,0.09580.0386,0.0317,0.0141,0.0958 , and 0.12010.1201 .

 Regression Statistics  Multiple R 0.7035 R Square 0.4949 Adjusted R 0.4030 Square  Standard 18.4861 Error  Observations 40\begin{array}{lr}\hline{\text { Regression Statistics }} \\\hline \text { Multiple R } & 0.7035 \\\text { R Square } & 0.4949 \\\text { Adjusted R } & 0.4030 \\\text { Square } & \\\text { Standard } & 18.4861 \\\text { Error } & \\\text { Observations } & 40 \\\hline\end{array}
 ANOVA \text { ANOVA }
 SCENARIO 17-10 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), the number of years of education received (Edu), the number of years at the previous job (Job Yr), a dummy variable for marital status (Married:  1 =  married,  0 =  otherwise), a dummy variable for head of household (Head:  1 =  yes,  0 =  no) and a dummy variable for management position (Manager:  1 =  yes,  0 =  no). We shall call this Model 1. The coefficient of partial determination (  R _ { \mathrm { y } } ^ { 2 }  (All raiables excopt  j  ) ) of each of the 6 predictors are, respectively,  0.2807 ,  0.0386,0.0317,0.0141,0.0958 , and  0.1201 .   \begin{array}{lr} \hline{\text { Regression Statistics }} \\ \hline \text { Multiple R } & 0.7035 \\ \text { R Square } & 0.4949 \\ \text { Adjusted R } & 0.4030 \\ \text { Square } & \\ \text { Standard } & 18.4861 \\ \text { Error } & \\ \text { Observations } & 40 \\ \hline \end{array}    \text { ANOVA }       \begin{array}{l} \begin{array} { l r r r r r r }  \hline & \text { Coefficients } & \text { Standard Error } & { t \text { Stat } } & \text { P-value } & \text { Lower 95\% } & \text { Upper 95\% } \\ \hline \text { Intercept } & 32.6595 & 23.18302 & 1.4088 & 0.1683 & - 14.5067 & 79.8257 \\ \text { Age } & 1.2915 & 0.3599 & 3.5883 & 0.0011 & 0.5592 & 2.0238 \\ \text { Edu } & - 1.3537 & 1.1766 & - 1.1504 & 0.2582 & - 3.7476 & 1.0402 \\ \text { Job Yr } & 0.6171 & 0.5940 & 1.0389 & 0.3064 & - 0.5914 & 1.8257 \\ \text { Married } & - 5.2189 & 7.6068 & - 0.6861 & 0.4974 & - 20.6950 & 10.2571 \\ \text { Head } & - 14.2978 & 7.6479 & - 1.8695 & 0.0704 & - 29.8575 & 1.2618 \\ \text { Manager } & - 24.8203 & 11.6932 & - 2.1226 & 0.0414 & - 48.6102 & - 1.0303 \\ \hline \end{array} \end{array}  -Referring to Scenario 17-10 and using both Model 1 and Model 2, there is sufficient evidence to conclude that at least one of the independent variables that are not significant individually has become significant as a group in explaining the variation in the dependent variable at a 5% level of significance?


 Coefficients  Standard Error t Stat  P-value  Lower 95%  Upper 95%  Intercept 32.659523.183021.40880.168314.506779.8257 Age 1.29150.35993.58830.00110.55922.0238 Edu 1.35371.17661.15040.25823.74761.0402 Job Yr 0.61710.59401.03890.30640.59141.8257 Married 5.21897.60680.68610.497420.695010.2571 Head 14.29787.64791.86950.070429.85751.2618 Manager 24.820311.69322.12260.041448.61021.0303\begin{array}{l}\begin{array} { l r r r r r r } \hline & \text { Coefficients } & \text { Standard Error } & { t \text { Stat } } & \text { P-value } & \text { Lower 95\% } & \text { Upper 95\% } \\\hline \text { Intercept } & 32.6595 & 23.18302 & 1.4088 & 0.1683 & - 14.5067 & 79.8257 \\\text { Age } & 1.2915 & 0.3599 & 3.5883 & 0.0011 & 0.5592 & 2.0238 \\\text { Edu } & - 1.3537 & 1.1766 & - 1.1504 & 0.2582 & - 3.7476 & 1.0402 \\\text { Job Yr } & 0.6171 & 0.5940 & 1.0389 & 0.3064 & - 0.5914 & 1.8257 \\\text { Married } & - 5.2189 & 7.6068 & - 0.6861 & 0.4974 & - 20.6950 & 10.2571 \\\text { Head } & - 14.2978 & 7.6479 & - 1.8695 & 0.0704 & - 29.8575 & 1.2618 \\\text { Manager } & - 24.8203 & 11.6932 & - 2.1226 & 0.0414 & - 48.6102 & - 1.0303 \\\hline\end{array}\end{array}
-Referring to Scenario 17-10 and using both Model 1 and Model 2, there is
sufficient evidence to conclude that at least one of the independent variables that are not
significant individually has become significant as a group in explaining the variation in the
dependent variable at a 5% level of significance?


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