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):x100125125150150200200250250300300350400400y140150180210190310270410440450380600600670 (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 200? (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 60. (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 = No, emission rate (ppm):100125 125 150 150 200 200 250 250 300 300 350 400 400y 140 150 180 210 190 310 270 410 440 450 380 600 600 670n USE SALT(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 NO, emission rate when burner area liberation rate equals 200? (Round your answer to two decimal places.)ppm(c) Estimate the amount by which you expect NO, emission rate to change when burner area liberation rate is decreased by 60. (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?O Yes, the data is perfectly linear, thus lending to accurate predictions.O Yes, this value is between two existing values.O No, this value is too far away from the known values for useful extrapolation.O No, the data near this point deviates from the overall regression model.

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Elementary Linear Algebra (MindTap Course List)

8th Edition

ISBN:9781305658004

Author:Ron Larson

Publisher:Ron Larson

Chapter2: Matrices

Section2.CR: Review Exercises

Problem 90CR

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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 = NO_x emission rate (ppm):

x	100	125	125	150	150	200	200	250	250	300	300	350	400	400
y	140	150	180	210	190	310	270	410	440	450	380	600	600	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 NO_x emission rate when burner area liberation rate equals 200? (Round your answer to two decimal places.)
ppm

(c) Estimate the amount by which you expect NO_x emission rate to change when burner area liberation rate is decreased by 60. (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.