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In Any Production Process in Which One or More Workers E(y)=β0+β1x1+β2x2E ( y ) = \beta _ { 0 } + \beta _ { 1 } x _ { 1 } + \beta _ { 2 } x _ { 2 }

question 92

Essay

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 (x1) and the number of checks cashed per day (x2). Data collected for n = 20 working days were used to fit the model: E(y)=β0+β1x1+β2x2E ( y ) = \beta _ { 0 } + \beta _ { 1 } x _ { 1 } + \beta _ { 2 } x _ { 2 }
A printout for the analysis follows:

\quad \quad \quad \quad \quad \quad \quad \quad \quad \quad  Analysis of Variance \text { Analysis of Variance }
 SOURCE  DF  SS  MS  FVALUE  PROB >F\begin{array}{llllll}\text { SOURCE } & \text { DF } & \text { SS } & \text { MS } & \text { FVALUE } & \text { PROB }>F\end{array}

 MODEL 27089.065123544.5325613.2670.0003 ERROR 174541.72142267.16008 C TOTAL 1911630.78654\begin{array}{lrrrrr}\text { MODEL } & 2 & 7089.06512 & 3544.53256 & 13.267 & 0.0003 \\\text { ERROR } & 17 & 4541.72142 & 267.16008 & & \\\text { C TOTAL } & 19 & 11630.78654 & & &\end{array}

\quad \quad \quad \quad \quad \quad \quad \quad \quad \quad \quad \quad \quad \quad \quad  Parameter Estimates \text { Parameter Estimates }

 PARAMETER  STANDARD  TFOR 0:  VARIABLE  DF  ESTIMATE  ERROR  PARAMETER =0 PROB >T\begin{array}{rrrrl} & \text { PARAMETER } & \text { STANDARD } & \text { TFOR 0: } \\\text { VARIABLE } \text { DF } & \text { ESTIMATE } & \text { ERROR } & \text { PARAMETER }=0 \quad \text { PROB }>|T|\end{array}

 INTERCEPT 1114.42097218.684857446.1240.0001 X1 10.0071020.001713754.1440.0007 X2 10.0372900.020439371.8240.0857\begin{array}{lrrrrr}\text { INTERCEPT } & 1 & 114.420972 & 18.68485744 & 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}

 ActualPredict Lower 95 % CL Upper 95 % CL OBS  X1  X2  Value  Value  Residual  Predict  Predict 1778164474.70783.1758.46847.224119.126\begin{array}{lrrrrrrr}&&&\text { Actual}& \text {Predict }&&\text {Lower 95 \% CL}& \text { Upper 95 \% CL}\\\text { OBS } & \text { X1 } & \text { X2 } & \text { Value } & \text { Value } & \text { Residual } & \text { Predict } & \text { Predict } \\1 & 7781 & 644 & 74.707 & 83.175 & -8.468 & 47.224 & 119.126 \\\hline\end{array}

Test to determine if there is a positive linear relationship between the number of man-hours worked, yy , and the number of checks cashed per day, x2x _ { 2 } . Use α=.05\alpha = .05 .


Definitions:

Principal Balance

The remaining amount of money borrowed or invested, excluding any interest or fees.

Compounded Semi-annually

Interest on an investment or loan is calculated and added to the principal twice a year.

Amortization Schedule

A table detailing each periodic payment on an amortizing loan, showing amounts toward principal and interest and the remaining balance after each payment.

Compounded Semi-annually

An interest calculation method in which interest is added to the principal balance twice a year, leading to exponential growth.

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