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A production plant cost-control engineer is responsible for cost reduction. One of the costly items in his plant is the amount of water used by the production facilities each month. He decided to investigate water usage by collecting seventeen observations orn his plant's water usage and other variables
Variable Description
Temperature Average monthly temperate (F)
Production Amount of production (pounds)
Days Number of plant operating days in the month
Persons Number of persons on the monthly plant payroll
Water Monthly water usage (gallons)
The output is shown below Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations 0.876 0.767 0.689 17 ANOVA cance F Regression Residual Total 4 12 16 2448834.0 612208.5 743797.5 61983.1 192631.5 9.88 0.0009 Coefficients 6360.34 13.87 0.21 126.69 21.82 Standard Error 1314.39 5.16 0.05 48.02 7.28 t Stat P-value 4.84 0.0004 .69 0.0197 .65 0.0006 2.64 0.0216 .00 0.0112 Lower 95% 3496.5 Upper 95% 9224.15 25.11 0.31 22.06 5.95 Lower 95.0% 3496.52 2.63 0.11 231.32 37.69 uper 95.0% 9224.15 25.11 0.31 22.06 5.95 Intercept Temperature Production 2.63 0.11 -231.32 37.69 Persons
What is a, the standard error of the estimate? Include 2 decimal places.

Sagot :

ogorwyne

Answer:

248.96

Step-by-step explanation:

From this regression output we have the MS Residual or mean squared error to be equal to 61983.1

the question requires us to find the standard error of the estimate. The standard error of the estimate can be gotten by finding the square root of the MSE.

[tex]\sqrt{61983.1}[/tex]

= 248.96

the standard error of the estimate = 248.96

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