Carlos Cavazos, Director of Human Resources, is exploring employee absenteeism at the Plano Piano Plant.A multiple regression analysis was performed using the following variables.The results are presented below. $$\begin{array} { | l | l | }
\hline \text { Variable } & \text { Description } \
\hline Y & \text { number of days absent last fiscal year } \
\hline x _ { 1 } & \text { comrnuting distarnce (in miles) } \
\hline x _ { 2 } & \text { employee's age (in years) } \
\hline x _ { 3 } & \text { single-parent household } ( 0 = \text { no, } 1 = \text { yes } ) \
\hline x _ { 4 } & \text { length of employment at PpP (in years) } \
\hline x _ { 5 } & \text { shift } ( 0 = \text { day } 1 = \text { night) } \
\hline
\end{array}$$ $$\begin{array} { | c | c | c | c | c | }
\hline & \text { Coefficients } & \text { Standard Error } & t \text { Statistic } & p \text {-value } \
\hline \text { Intercept } & 6.594146 & \mathbf { 3 . 2 7 3 0 0 5 } & \mathbf { 2 . 0 1 4 7 0 7 } & \mathbf { 0 . 0 4 7 6 7 1 } \
\hline \boldsymbol { x } _ { 1 } & - 0.18019 & 0.141949 & - 1.26939 & 0.208391 \
\hline \mathbf { x } _ { 2 } & 0.268156 & 0.260643 & 1.028828 & 0.307005 \
\hline \boldsymbol { x } _ { 3 } & - 2.31068 & 0.962056 & - 2.40182 & 0.018896 \
\hline \mathbf { x } _ { 4 } & - 0.50579 & 0.270872 & - 1.86725 & 0.065937 \
\hline \boldsymbol { x } _ { 5 } & \mathbf { 2 . 3 2 9 5 1 3 } & 0.940321 & 2.47736 & 0.015584 \
\hline
\end{array}$$ $$\begin{array} { | c | c | c | c | c | c | }
\hline & \mathrm { df } & \text { SS } & \text { ME } & F & p \text {-value } \
\hline \text { Repression } & 5 & 279.358 & 55.8716 & 4.423755 & \mathbf { 0 . 0 0 1 5 3 2 } \
\hline \text { Residual } & 67 & 846.2036 & 12.6299 & & \
\hline \text { Total } & 72 & 1125.562 & & & \
\hline
\end{array}$$ $$\begin{array} { | c | c | c | }
\hline R = 0.498191 & R ^ { 2 } = 0.248194 & \text { Adj } R ^ { 2 } = 0.192089 \
\hline \mathrm { s } _ { \mathrm { e } } = 3.553858 & n = 73 & \
\hline
\end{array}$$ Which of the following conclusions can be drawn from the above results?
A)All the independent variables in the regression are significant at 5% level.
B)Commuting distance is a highly significant (

Question

Carlos Cavazos, Director of Human Resources, is exploring employee absenteeism at the Plano Piano Plant.A multiple regression analysis was performed using the following variables.The results are presented below. $$\begin{array} { | l | l | } 
\hline \text { Variable } & \text { Description } \
\hline Y & \text { number of days absent last fiscal year } \
\hline x _ { 1 } & \text { comrnuting distarnce (in miles) } \
\hline x _ { 2 } & \text { employee's age (in years) } \
\hline x _ { 3 } & \text { single-parent household } ( 0 = \text { no, } 1 = \text { yes } ) \
\hline x _ { 4 } & \text { length of employment at PpP (in years) } \
\hline x _ { 5 } & \text { shift } ( 0 = \text { day } 1 = \text { night) } \
\hline
\end{array}$$ $$\begin{array} { | c | c | c | c | c | } 
\hline & \text { Coefficients } & \text { Standard Error } & t \text { Statistic } & p \text {-value } \
\hline \text { Intercept } & 6.594146 & \mathbf { 3 . 2 7 3 0 0 5 } & \mathbf { 2 . 0 1 4 7 0 7 } & \mathbf { 0 . 0 4 7 6 7 1 } \
\hline \boldsymbol { x } _ { 1 } & - 0.18019 & 0.141949 & - 1.26939 & 0.208391 \
\hline \mathbf { x } _ { 2 } & 0.268156 & 0.260643 & 1.028828 & 0.307005 \
\hline \boldsymbol { x } _ { 3 } & - 2.31068 & 0.962056 & - 2.40182 & 0.018896 \
\hline \mathbf { x } _ { 4 } & - 0.50579 & 0.270872 & - 1.86725 & 0.065937 \
\hline \boldsymbol { x } _ { 5 } & \mathbf { 2 . 3 2 9 5 1 3 } & 0.940321 & 2.47736 & 0.015584 \
\hline
\end{array}$$ $$\begin{array} { | c | c | c | c | c | c | } 
\hline & \mathrm { df } & \text { SS } & \text { ME } & F & p \text {-value } \
\hline \text { Repression } & 5 & 279.358 & 55.8716 & 4.423755 & \mathbf { 0 . 0 0 1 5 3 2 } \
\hline \text { Residual } & 67 & 846.2036 & 12.6299 & & \
\hline \text { Total } & 72 & 1125.562 & & & \
\hline
\end{array}$$ $$\begin{array} { | c | c | c | } 
\hline R = 0.498191 & R ^ { 2 } = 0.248194 & \text { Adj } R ^ { 2 } = 0.192089 \
\hline \mathrm { s } _ { \mathrm { e } } = 3.553858 & n = 73 & \
\hline
\end{array}$$ Which of the following conclusions can be drawn from the above results?
A)All the independent variables in the regression are significant at 5% level.
B)Commuting distance is a highly significant (<1%)variable in explaining absenteeism.
C)Age of the employees tends to have a very significant (<1%)effect on absenteeism.
D)This model explains a little over 49% of the variability in absenteeism data.
E)A single-parent household employee is expected to be absent fewer days, all other variables held constant, compared to one who is not a single-parent household.

Quizplus · Accepted Answer

The Answer of Carlos Cavazos, Director of Human Resources, is exploring employee absenteeism at the Plano Piano Plant.A multiple regression analysis was performed using the following variables.The results are presented below. $$\begin{array} { | l | l | } 
\hline \text { Variable } & \text { Description } \
\hline Y & \text { number of days absent last fiscal year } \
\hline x _ { 1 } & \text { comrnuting distarnce (in miles) } \
\hline x _ { 2 } & \text { employee's age (in years) } \
\hline x _ { 3 } & \text { single-parent household } ( 0 = \text { no, } 1 = \text { yes } ) \
\hline x _ { 4 } & \text { length of employment at PpP (in years) } \
\hline x _ { 5 } & \text { shift } ( 0 = \text { day } 1 = \text { night) } \
\hline
\end{array}$$ $$\begin{array} { | c | c | c | c | c | } 
\hline & \text { Coefficients } & \text { Standard Error } & t \text { Statistic } & p \text {-value } \
\hline \text { Intercept } & 6.594146 & \mathbf { 3 . 2 7 3 0 0 5 } & \mathbf { 2 . 0 1 4 7 0 7 } & \mathbf { 0 . 0 4 7 6 7 1 } \
\hline \boldsymbol { x } _ { 1 } & - 0.18019 & 0.141949 & - 1.26939 & 0.208391 \
\hline \mathbf { x } _ { 2 } & 0.268156 & 0.260643 & 1.028828 & 0.307005 \
\hline \boldsymbol { x } _ { 3 } & - 2.31068 & 0.962056 & - 2.40182 & 0.018896 \
\hline \mathbf { x } _ { 4 } & - 0.50579 & 0.270872 & - 1.86725 & 0.065937 \
\hline \boldsymbol { x } _ { 5 } & \mathbf { 2 . 3 2 9 5 1 3 } & 0.940321 & 2.47736 & 0.015584 \
\hline
\end{array}$$ $$\begin{array} { | c | c | c | c | c | c | } 
\hline & \mathrm { df } & \text { SS } & \text { ME } & F & p \text {-value } \
\hline \text { Repression } & 5 & 279.358 & 55.8716 & 4.423755 & \mathbf { 0 . 0 0 1 5 3 2 } \
\hline \text { Residual } & 67 & 846.2036 & 12.6299 & & \
\hline \text { Total } & 72 & 1125.562 & & & \
\hline
\end{array}$$ $$\begin{array} { | c | c | c | } 
\hline R = 0.498191 & R ^ { 2 } = 0.248194 & \text { Adj } R ^ { 2 } = 0.192089 \
\hline \mathrm { s } _ { \mathrm { e } } = 3.553858 & n = 73 & \
\hline
\end{array}$$ Which of the following conclusions can be drawn from the above results?
A)All the independent variables in the regression are significant at 5% level.
B)Commuting distance is a highly significant (<1%)variable in explaining absenteeism.
C)Age of the employees tends to have a very significant (<1%)effect on absenteeism.
D)This model explains a little over 49% of the variability in absenteeism data.
E)A single-parent household employee is expected to be absent fewer days, all other variables held constant, compared to one who is not a single-parent household.

Carlos Cavazos, Director of Human Resources, Is Exploring Employee Absenteeism