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Operations Managers Often Use Work Sampling to Estimate How Much E(y)=β0+β1xE ( y ) = \beta _ { 0 } + \beta _ { 1 } ^ { x }

question 68

Essay

Operations managers often use work sampling to estimate how much time workers spend on each operation. Work sampling-which involves observing workers at random points in
time-was applied to the staff of the catalog sales department of a clothing manufacturer.
The department applied regression to data collected for 40 randomly selected working days.
The simple linear model E(y)=β0+β1xE ( y ) = \beta _ { 0 } + \beta _ { 1 } ^ { x } was fit to the data. The printouts for the analysis are given below:

TIME: y=\quad y = Time spent (in hours) taking telephone orders during the day
ORDERS: x=\quad x = Number of telephone orders received during the day

UNWEIGHTED LEAST SQUARES LINEAR REGRESSION OF TIME

 PREDICTOR  VARIABLES  COEFFICIENT  STD ERROR  STUDENT’S T  P  CONSTANT 10.16391.778445.720.0000 ORDERS 0.058360.005869.960.0000\begin{array}{l|c|c|c|c}\text { PREDICTOR } & & & & \\\text { VARIABLES } & \text { COEFFICIENT } & \text { STD ERROR } & \text { STUDENT'S T } & \text { P } \\\hline \text { CONSTANT } & 10.1639 & 1.77844 & 5.72 & 0.0000 \\\text { ORDERS } & 0.05836 & 0.00586 & 9.96 & 0.0000\end{array}

 R-SQUARED 0.7229 RESID. MEAN SQUARE (MSE) 11.6175 ADJUSTED R-SQUARED 0.7156 STANDARD DEVIATION 3.40844\begin{array}{lllr}\text { R-SQUARED } & 0.7229 & \text { RESID. MEAN SQUARE (MSE) } & 11.6175 \\\text { ADJUSTED R-SQUARED } & 0.7156 & \text { STANDARD DEVIATION } & 3.40844\end{array}


 SOURCE  DF  SS  MS FP REGRESSION 11151.551151.5599.120.0000 RESIDUAL 38441.46411.6175 TOTAL 391593.01\begin{array}{l|r|r|r|c|c}\text { SOURCE } & \text { DF } &{\text { SS }} & \text { MS } & \mathrm{F} & \mathrm{P} \\\hline \text { REGRESSION } & 1 & 1151.55 & 1151.55 & 99.12 & 0.0000 \\\text { RESIDUAL } & 38 & 441.464 & 11.6175 & & \\\text { TOTAL } & 39 & 1593.01 & & &\end{array}

CASES INCLUDED 40 \quad MISSING CASES 0

Conduct a test of hypothesis to determine if time spent (in hours) taking telephone orders during the day and the number of telephone orders received during the day are positively linearly related. Use α=.01\alpha = .01 .


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