The following data is representative of that reported in anarticle on nitrogen emissions, with x = burner arealiberation rate (MBtu/hr-ft2) and y =NOx emission rate (ppm):
| x | 100 | 125 | 125 | 150 | 150 | 200 | 200 | 250 | 250 | 300 | 300 | 350 | 400 | 400 |
| y | 140 | 140 | 170 | 210 | 200 | 330 | 280 | 390 | 440 | 450 | 400 | 590 | 610 | 660 |
(a) Assuming that the simple linear regression model is valid,obtain the least squares estimate of the true regression line.(Round all numerical values to four decimal places.)
y =
(b) What is the estimate of expected NOxemission rate when burner area liberation rate equals 215? (Roundyour answer to two decimal places.)
ppm
(c) Estimate the amount by which you expect NOxemission rate to change when burner area liberation rate isdecreased by 60. (Round your answer to two decimal places.)
ppm
(d) Would you use the estimated regression line to predict emissionrate for a liberation rate of 500? Why or why not?
Yes, the data is perfectly linear, thus lending to accuratepredictions.
Yes, this value is between two existingvalues.
No, this value is too far away from the known values for usefulextrapolation.
No, the data near this point deviates from the overallregression model.
