The following computer printout estimated overhead costs using multiple regression: $\begin{array}{lrrrr}
&&\text { t for H(0) }&&\text {Std. error}\
\text {Parameter}&\text { Estimate } &\text { Parameter }=0 & \text { P r}t & \text { of parameter }\
\hline\text { Intercept } & 1000 & 1.96 & 0.0250 & 510.204 \
\text { Setup hours } & 25 & 81.96 & 0.0001 & 0.305 \
\text { \# of parts } & 100 & 9.50 & 0.0001 & 10.527
\end{array}$

$\begin{array}{lc}
\text { R Square }\left(\mathrm{R}^{2}\right) & 0.94 \
\text { Standard Error }\left(\mathrm{S}_{\mathrm{e}}\right) & 75.00 \
\text { Observations } & 160
\end{array}$ Which slope and intercept parameters are significant at the 0.05 level?
A)intercept
B)setup hours
C)number of parts
D)All of the above are significant

Question

The following computer printout estimated overhead costs using multiple regression: $\begin{array}{lrrrr}
&&\text { t  for H(0) }&&\text {Std. error}\
\text {Parameter}&\text { Estimate } &\text { Parameter }=0 & \text { P r}>t & \text { of parameter }\
\hline\text { Intercept } & 1000 & 1.96 & 0.0250 & 510.204 \
\text { Setup hours } & 25 & 81.96 & 0.0001 & 0.305 \
\text { \# of parts } & 100 & 9.50 & 0.0001 & 10.527
\end{array}$

$\begin{array}{lc}
\text { R Square }\left(\mathrm{R}^{2}\right) & 0.94 \
\text { Standard Error }\left(\mathrm{S}_{\mathrm{e}}\right) & 75.00 \
\text { Observations } & 160
\end{array}$ Which slope and intercept parameters are significant at the 0.05 level?
A)intercept
B)setup hours
C)number of parts
D)All of the above are significant

Quizplus · Accepted Answer

Since the question asks for all significant parameters at the 0.05 level, we need to look for those with a p-value less than or equal to 0.05. Without the p-values or additional information, we cannot determine which specific parameters are significant, but we can conclude that all of the above parameters (intercept, setup hours, and number of parts) are significant at the 0.05 level.

The Following Computer Printout Estimated Overhead Costs Using Multiple Regression