In any production process in which one or more workers are engaged in a variety of tasks, the total time spent in production varies as a function of the size of the workpool and the level of output of the various activities. In a large metropolitan department store, it is believed that the number of man-hours worked

( y )

per day by the clerical staff depends on the number of pieces of mail processed per day

\left( x _ { 1 } \right)

and the number of checks cashed per day

\left( x _ { 2 } \right)

. Data collected for

n = 20

working days were used to fit the model:

E ( y ) = \beta _ { 0 } + \beta _ { 1 } x _ { 1 } + \beta _ { 2 } x _ { 2 }

A partial printout for the analysis follows:

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\text{Parameter Estimates}

\begin{array} { l r r r r r } & & \text { PARAMETER } & \text { STANDARD } & \text { T FOR 0: } & \\ \text { VARIABLE } & \text { DF } & \text { ESTIMATE } & \text { ERROR } & \text { PARAMETER = 0 } & \text { PROB > |T| } \\ & & & & & \\ \text { INTERCEPT } & 1 & 114.420972 & 18.6848744 & 6.124 & 0.0001 \\ \text { X1 } & 1 & - 0.007102 & 0.00171375 & - 4.144 & 0.0007 \\ \text { X2 } & 1 & 0.037290 & 0.02043937 & 1.824 & 0.0857 \end{array}

Calculate a

95 \%

confidence interval for

\beta _ { 1 }

.
A)

- 4.144 \pm .0007

- .007 \pm .0007

- .007 \pm .0017

- .007 \pm .0036

When Using the Model E(y)=β0+β1xE ( y ) = \beta _ { 0 } + \beta _ { 1 } xE(y)=β0​+β1​x

True/False

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When Using the Model $E ( y ) = \beta _ { 0 } + \beta _ { 1 } x$

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