Using the sample data from the accompanying table, complete parts (a) and (b). ... (a) Explain why it does not make sense to construct confidence or prediction intervals based on the least-squares regression equation. Choose the correct answer below. OA. It does not make sense to construct confidence or prediction intervals based on the least-squares regression equation because there is a linear relationship between sugar content and calories in high-protein and moderate protein energy bars. OB. It does not make sense to construct confidence or prediction intervals based on the least-squares regression equation because there is no linear relationship between sugar content and calories in high-protein and moderate protein energy bars. OC. It does not make sense to construct confidence or prediction intervals based on the least-squares regression equation because the residuals are not normally distributed. (b) Construct a 95% confidence interval for the mean sugar content of energy bars. The 95% confidence interval for the mean sugar content of energy bars is lower bound: upper bound: (Round to one decimal place as needed.)
Using the sample data from the accompanying table, complete parts (a) and (b). ... (a) Explain why it does not make sense to construct confidence or prediction intervals based on the least-squares regression equation. Choose the correct answer below. OA. It does not make sense to construct confidence or prediction intervals based on the least-squares regression equation because there is a linear relationship between sugar content and calories in high-protein and moderate protein energy bars. OB. It does not make sense to construct confidence or prediction intervals based on the least-squares regression equation because there is no linear relationship between sugar content and calories in high-protein and moderate protein energy bars. OC. It does not make sense to construct confidence or prediction intervals based on the least-squares regression equation because the residuals are not normally distributed. (b) Construct a 95% confidence interval for the mean sugar content of energy bars. The 95% confidence interval for the mean sugar content of energy bars is lower bound: upper bound: (Round to one decimal place as needed.)
Calculus For The Life Sciences
2nd Edition
ISBN:9780321964038
Author:GREENWELL, Raymond N., RITCHEY, Nathan P., Lial, Margaret L.
Publisher:GREENWELL, Raymond N., RITCHEY, Nathan P., Lial, Margaret L.
Chapter1: Functions
Section1.CR: Chapter 1 Review
Problem 86CR
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Transcribed Image Text:Using the sample data from the accompanying table, complete parts (a) and (b).
C...
(a) Explain why it does not make sense to construct confidence or prediction intervals based on the least-squares regression equation.
Choose the correct answer below.
OA. It does not make sense to construct confidence or prediction intervals based on the least-squares regression equation because
there is a linear relationship between sugar content and calories in high-protein and moderate protein energy bars.
OB. It does not make sense to construct confidence or prediction intervals based on the least-squares regression equation because
there is no linear relationship between sugar content and calories in high-protein and moderate protein energy bars.
OC. It does not make sense to construct confidence or prediction intervals based on the least-squares regression equation because the
residuals are not normally distributed.
(b) Construct a 95% confidence interval for the mean sugar content of energy bars.
The 95% confidence interval for the mean sugar content of energy bars is
lower bound:
upper bound:.
(Round to one decimal place as needed.)
Transcribed Image Text:Data Table
Calories, x
180
200
210
220
220
230
240
Sugar, y
11
18
14
20
0
28
2
Question 6 of 10
Calories, x
270
320
110
180
200
220
230
Print
Sugar, y
>
Done
2N22222
20
10
12
24
24
It can be shown that there is no linear relationship between sugar content and
calories in high-protein and moderate protein energy bars.
Full data set
This qui:
This que
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