A manufacturer of boiler drums wants to use regression to predict the number of man-hours needed to erect drums in the future. The manufacturer collected a random sample of 35 boilers and measured the following two variables:
MANHRS: y = Number of man-hours required to erect the drum
PRESSURE: x
1= Boiler design pressure (pounds per square inch, i.e., psi)
Initially, the simple linear model E(y) = β
0 + β
1x1 was fit to the data. A printout for the analysis appears below:
UNWEIGHTED LEAST SQUARES LINEAR REGRESSION OF MANHRS
Give a practical interpretation of the coefficient of determination, R
2. Express R
2 to the nearest whole percent.
◦ About 43% of the sample variation in number of man-hours can be explained by the simple linear model.
◦ About 2.06% of the sample variation in number of man-hours can be explained by the simple linear model.
◦
= 1.88 + 0.00321x will be correct 43% of the time.
◦ Man hours needed to erect drums will be associated with boiler design pressure 43% of the time.
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