Question

The following data is representative of that reported in an article on nitrogen emissions, with x = burner area liberation 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 | 140 150 180 210 200 310 290 410 420 440 390 610 610 670 (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 NOx emission rate when burner area liberation rate equals 205? (Round your answer to two decimal places.) ppm (c) Estimate the amount by which you expect NOx emission rate to change when burner area liberation rate is decreased by 50. (Round your answer to two decimal places.) ppm (d) Would you use the estimated regression line to predict emission rate for a liberation rate of 500? Why or why not? Yes, the data is perfectly linear, thus lending to accurate predictions. Yes, this value is between two existing values. No, this value is too far away from the known values for useful extrapolation. No, the data near this point deviates from the overall regression model.

          The following data is representative of that reported in an article on nitrogen emissions, with x = burner area liberation 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 | 140 150 180 210 200 310 290 410 420 440 390 610 610 670

(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 NOx emission rate when burner area liberation rate equals 205? (Round your answer to two decimal places.)
ppm

(c) Estimate the amount by which you expect NOx emission rate to change when burner area liberation rate is decreased by 50. (Round your answer to two decimal places.)
ppm

(d) Would you use the estimated regression line to predict emission rate for a liberation rate of 500? Why or why not?
Yes, the data is perfectly linear, thus lending to accurate predictions.
Yes, this value is between two existing values.
No, this value is too far away from the known values for useful extrapolation.
No, the data near this point deviates from the overall regression model.

The following data is representative of that reported in an article on nitrogen emissions, with x = burner area liberation 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 | 140 150 180 210 200 310 290 410 420 440 390 610 610 670

(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 NOx emission rate when burner area liberation rate equals 205? (Round your answer to two decimal places.)
ppm

(c) Estimate the amount by which you expect NOx emission rate to change when burner area liberation rate is decreased by 50. (Round your answer to two decimal places.)
ppm

(d) Would you use the estimated regression line to predict emission rate for a liberation rate of 500? Why or why not?
Yes, the data is perfectly linear, thus lending to accurate predictions.
Yes, this value is between two existing values.
No, this value is too far away from the known values for useful extrapolation.
No, the data near this point deviates from the overall regression model.

Added by Cassidy G.

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