Using the sample data from the accompanying table, complete parts (a) and (b). E Click the icon to view the data table.

MATLAB: An Introduction with Applications
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ISBN:9781119256830
Author:Amos Gilat
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Chapter1: Starting With Matlab
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Using the sample data from the accompanying table, complete parts (a) and (b).
E Click the icon to view the data table
(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.
O A. 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.
O B. 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.
O C. 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.
- X
The 95% confidence interval for the mean sugar content of energy bars is
Data Table
lower bound: upper bound:
(Round to one decimal place as needed.)
Full data set O
Calories, x
Sugar, y
Calories, x
Sugar, y
190
11
270
20
200
19
320
2
210
14
110
10
220
20
180
12
220
200
22
230
28
220
24
240
2
230
24
It can be shown that there is no linear relationship between sugar content and
calories in high-protein and moderate protein energy bars.
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Transcribed Image Text:Using the sample data from the accompanying table, complete parts (a) and (b). E Click the icon to view the data table (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. O A. 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. O B. 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. O C. 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. - X The 95% confidence interval for the mean sugar content of energy bars is Data Table lower bound: upper bound: (Round to one decimal place as needed.) Full data set O Calories, x Sugar, y Calories, x Sugar, y 190 11 270 20 200 19 320 2 210 14 110 10 220 20 180 12 220 200 22 230 28 220 24 240 2 230 24 It can be shown that there is no linear relationship between sugar content and calories in high-protein and moderate protein energy bars. Print Done
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