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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 = NOx emission rate (ppm): X 100 125 125 150 150 200 200 250 250 300 300 350 400 400 y 150 150 190 210 190 330 290 410 430 430 390 610 610 670 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 = -39.251 + 1.7059 X Check the degree of your polynomial. (b) What is the estimate of expected NOx emission rate when burner area liberation rate equals 245? (Round your answer to two decimal places.) 361.63 x 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.) 28.98 x 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. o Yes, this value is between two existing values. o 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 150 150 190 210 190 330 290 410 430 430 390 610 610 670 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 = -39.251 + 1.7059 X Check the degree of your polynomial. (b) What is the estimate of expected NOx emission rate when burner area liberation rate equals 245? (Round your answer to two decimal places.) 361.63 x 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.) 28.98 x 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. o Yes, this value is between two existing values. o 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.

A First Course in Probability (10th Edition)
A First Course in Probability (10th Edition)
10th Edition
ISBN:9780134753119
Author:Sheldon Ross
Publisher:Sheldon Ross
Chapter1: Combinatorial Analysis
Section: Chapter Questions
Problem 1.1P: a. How many different 7-place license plates are possible if the first 2 places are for letters and...
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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 = NOx emission rate (ppm): X 100 125 125 150 150 200 200 250 250 300 300 350 400 400 y 150 150 190 210 190 330 290 410 430 430 390 610 610 670 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 = -39.251 + 1.7059 X Check the degree of your polynomial. (b) What is the estimate of expected NOx emission rate when burner area liberation rate equals 245? (Round your answer to two decimal places.) 361.63 x 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.) 28.98 x 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. o Yes, this value is between two existing values. o 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.
Transcribed Image Text: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 150 150 190 210 190 330 290 410 430 430 390 610 610 670 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 = -39.251 + 1.7059 X Check the degree of your polynomial. (b) What is the estimate of expected NOx emission rate when burner area liberation rate equals 245? (Round your answer to two decimal places.) 361.63 x 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.) 28.98 x 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. o Yes, this value is between two existing values. o 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.
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