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5. For page 18 of chapter 9 notes, an example asks for a confidence interval for the population slope B. What is the correct confidence interval for the slope ß? O (0.05137, 0.14702) (0.05629, 0.14814) (0.05429, 0.14514) O (0.05891, 0.14238)

5. For page 18 of chapter 9 notes, an example asks for a confidence interval for the population slope B. What is the correct confidence interval for the slope ß? O (0.05137, 0.14702) (0.05629, 0.14814) (0.05429, 0.14514) O (0.05891, 0.14238)

MATLAB: An Introduction with Applications
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
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Attached a picture of the notes in reference to the question, any help appreciated. Thanks.

5. For page 18 of chapter 9 notes, an example asks for a confidence interval for the population slope B. What is the correct confidence interval for the slope ß? (0.05137, 0.14702) (0.05629, 0.14814) (0.05429, 0.14514) (0.05891, 0.14238)
Transcribed Image Text:5. For page 18 of chapter 9 notes, an example asks for a confidence interval for the population slope B. What is the correct confidence interval for the slope ß? (0.05137, 0.14702) (0.05629, 0.14814) (0.05429, 0.14514) (0.05891, 0.14238)
The slope of the regression line. The slope, b, of a regression line model, represents the slope of the sample on the graph. Even the smallest error in the calculation of the slope can cause large errors in predictions. Since the sample slope may be different by a small amount from the population slope, it is important for us build a confidence interval for the population slope, ß, which is what the model value of b tries to predict. Example Construct a 95% confidence interval for the slope ß for the house price data below. Size (Sq. ft.) 2521 2555 2735 2846 3028 3049 3198 3198 Selling Price ($1000s) 400 426 428 435 469 475 488 455 Solution: We enter the x-values (size) into L1 and the y-values (price) into L2. Press STAT and highlight the TESTS menu and select LinRegTInt. Enter L1 in the Xlist field and L2 in the Ylist field. Enter 0.95 in the C-Level field. Select Calculate. The confidence interval is (_ .). This indicates that the expected population rate of change, ß, is between The sample estimate rate of change which is b = We interpret this confidence interval as the range of possible rates of change in selling price per each additional square foot of house size. and The uncertainty with this population slope is due to confounding variables that affect price other than house size, such as size of the lot, or the location of the neighborhood. ADDITIONAL NOTE. If the range of possible ß values contains zero, then = 0 is possible. When this occurs, it means that the output (price) remains constant and is not affected by the input (size). This is very bad result that means we cannot use house size to predict house price. We always check the confidence interval to see if it contains zero. When this occurs, we must write a sentence that explains that predictions cannot be made.
Transcribed Image Text:The slope of the regression line. The slope, b, of a regression line model, represents the slope of the sample on the graph. Even the smallest error in the calculation of the slope can cause large errors in predictions. Since the sample slope may be different by a small amount from the population slope, it is important for us build a confidence interval for the population slope, ß, which is what the model value of b tries to predict. Example Construct a 95% confidence interval for the slope ß for the house price data below. Size (Sq. ft.) 2521 2555 2735 2846 3028 3049 3198 3198 Selling Price ($1000s) 400 426 428 435 469 475 488 455 Solution: We enter the x-values (size) into L1 and the y-values (price) into L2. Press STAT and highlight the TESTS menu and select LinRegTInt. Enter L1 in the Xlist field and L2 in the Ylist field. Enter 0.95 in the C-Level field. Select Calculate. The confidence interval is (_ .). This indicates that the expected population rate of change, ß, is between The sample estimate rate of change which is b = We interpret this confidence interval as the range of possible rates of change in selling price per each additional square foot of house size. and The uncertainty with this population slope is due to confounding variables that affect price other than house size, such as size of the lot, or the location of the neighborhood. ADDITIONAL NOTE. If the range of possible ß values contains zero, then = 0 is possible. When this occurs, it means that the output (price) remains constant and is not affected by the input (size). This is very bad result that means we cannot use house size to predict house price. We always check the confidence interval to see if it contains zero. When this occurs, we must write a sentence that explains that predictions cannot be made.
Expert Solution
Step 1

Suppose x be the size 

And y be the selling price

We have given the house price data as below,

Size Selling Price
2521 400
2555 426
2735 428
2846 435
3028 469
3049 475
3198 488
3198 455
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