The following data is available of estimated overhead costs using linear regression:

\begin{array} { l c c c c } \text { Parameter } & \text { Estimate } & \begin{array} { c } \text { tfor H(0) } \\\text { Interameter } = \mathbf { 0 }\end{array} & \underline { \mathbf { P r } > \mathbf { t } } & \begin{array} { c } \text { Std. error } \\\text { of parameter }\end{array} \\\hline \text { Intercept } & 100.41 & 4.81 & 0.0003 & 20.88 \\\text { DLH } & 14.05 & 6.78 & 0.0001 & 2.07 \\\end{array}

\begin{array}{lr}R \text { Square }\left(\mathrm{R}^{2}\right) & 0.80 \\\text { Standard Error }\left(\mathrm{S}_{\mathrm{e}}\right) & 25.03 \\\text { Observations } & 17\end{array}

Table of Selected Values: t Distribution

\begin{array}{llll}\text { Degrees of }\\\text { Freedom }&90\%&95\%&99\%\\\hline15 & 1.753 & 2.131 & 2.947 \\16 & 1.746 & 2.120 & 2.921 \\17 & 1.740 & 2.110 & 2.898 \\18 & 1.734 & 2.101 & 2.878 \\19 & 1.729 & 2.093 & 2.861\end{array}

What is the interval around Y if 95 percent confidence is desired?
A)Y ± 20.024
B)Y ± 43.87759
C)Y ± 52.8133
D)Y ± 53.33893

An Important Type of Nonlinear Cost Curve Is the Learning

Multiple Choice