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During Its Manufacture, a Product Is Subjected to Four Different (y)( y )

question 17

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During its manufacture, a product is subjected to four different tests in sequential order. An efficiency expert claims that the fourth (and last) test is unnecessary since its results can be predicted based on the first three tests. To test this claim, multiple regression will be used to model Test4 score (y) ( y ) , as a function of Test1 score (x1) \left( x _ { 1 } \right) ) , Test 2 score (x2) \left( x _ { 2 } \right) , and Test3 score (x3) \left( x _ { 3 } \right) . [Note: All test scores range from 200 to 800 , with higher scores indicative of a higher quality product.] Consider the model:
E(y) =β1+β1x1+β2x2+β3x3E ( y ) = \beta _ { 1 } + \beta _ { 1 } x _ { 1 } + \beta _ { 2 } x _ { 2 } + \beta _ { 3 } x _ { 3 }
The first-order model was fit to the data for each of 12 units sampled from the production line. The results are summarized in the printout.

 SOURCE  DF  SS  MS  FVALUE  PROB > F  MODEL 31514175047218.16.0075 ERROR 8222312779 TOTAL 12173648\begin{array}{lrrrrr}\text { SOURCE } & \text { DF } & \text { SS } & \text { MS } & \text { FVALUE } & \text { PROB > F } \\\text { MODEL } & 3 & 151417 & 50472 & 18.16 & .0075 \\\text { ERROR } & 8 & 22231 & 2779 & & \\\text { TOTAL } & 12 & 173648 & & &\end{array}

 ROOT MSE 52.72 R-SQUARE 0.872 DEP MEAN 645.8 ADJ R-SQ 0.824\begin{array}{llll}\text { ROOT MSE } & 52.72 & \text { R-SQUARE } & 0.872 \\\text { DEP MEAN } & 645.8 & \text { ADJ R-SQ } & 0.824\end{array}

PARAMETER STANDARDT FOR 0:VARIABLES ESTIMATE  ERROR  PARAMETER =0 PROB >T\begin{array} { l l l l } & \text {PARAMETER }& \text {STANDARD}& \text {T FOR 0:}\\ \text {VARIABLES}&\text { ESTIMATE } & \text { ERROR } & \text { PARAMETER } = 0 & \text { PROB } > | T | \end{array}


 INTERCEPT 11.9880.500.150.885 X1(TEST1)  0.27450.11112.470.039 X2(TEST2)  0.37620.09863.820.005 X3(TEST3)  0.32650.08084.040.004\begin{array} { l r r r r } \text { INTERCEPT } & 11.98 & 80.50 & 0.15 & 0.885 \\\text { X1(TEST1) } & 0.2745 & 0.1111 & 2.47 & 0.039 \\\text { X2(TEST2) } & 0.3762 & 0.0986 & 3.82 & 0.005 \\\text { X3(TEST3) } & 0.3265 & 0.0808 & 4.04 & 0.004 \\\hline\end{array}




Compute a 95%95 \% confidence interval for β3\beta _ { 3 } .


Definitions:

Defined Benefit Pension Plan

A retirement plan where employer commitments are based on a formula considering factors like salary history and duration of employment, with the company bearing investment risk.

Prior Service Cost Amortization

The process of expensing the costs of retroactive benefits granted in a pension plan over a period of time.

Actuarially Determined Discount Rate

This refers to the interest rate used in the actuarial valuation to discount future pension obligations to present value, reflecting the expected rate at which the pension plan's liabilities can be funded or settled.

Pension Liability

The amount of money a company is required to pay into its employees' retirement plans in the future.

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