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X > y 4 6 4 -1 A. -0.885 OB. -0.995 O C. -0.778 OD. -0.671 13 - 13 10 -8 8 -4 7 -3 Pounding to three decimal places. 9 -6 11 - 11 12 - 12 50 1

X > y 4 6 4 -1 A. -0.885 OB. -0.995 O C. -0.778 OD. -0.671 13 - 13 10 -8 8 -4 7 -3 Pounding to three decimal places. 9 -6 11 - 11 12 - 12 50 1

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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**Compute the Linear Correlation Coefficient** For the given data below, round the linear correlation coefficient to three decimal places: \[ \begin{array}{c|cccccccccc} x & 4 & 6 & 13 & 10 & 8 & 7 & 9 & 11 & 12 & 5 \\ \hline y & 4 & -1 & -13 & -8 & -4 & -3 & -6 & -11 & -12 & 1 \\ \end{array} \] **Options:** - A. -0.885 - B. -0.995 - C. -0.778 - D. -0.671 **Correct Answer:** - B. -0.995
Transcribed Image Text:**Compute the Linear Correlation Coefficient** For the given data below, round the linear correlation coefficient to three decimal places: \[ \begin{array}{c|cccccccccc} x & 4 & 6 & 13 & 10 & 8 & 7 & 9 & 11 & 12 & 5 \\ \hline y & 4 & -1 & -13 & -8 & -4 & -3 & -6 & -11 & -12 & 1 \\ \end{array} \] **Options:** - A. -0.885 - B. -0.995 - C. -0.778 - D. -0.671 **Correct Answer:** - B. -0.995
The data below represent the number of absences and the corresponding final grades of 9 randomly selected students from a literature class. The task is to find the least-squares regression line for the given data. Additionally, determine what the predicted final grade would be if a student was absent 14 times. The values for the least-squares regression line should be rounded to two decimal places, and the predicted grade should be rounded to the nearest whole number. ### Data: - **Number of absences, x:** 0, 3, 6, 4, 9, 2, 15, 8, 5 - **Final grade, y:** 98, 86, 80, 82, 71, 92, 55, 76, 82 ### Options for Least-Squares Regression Line: A. \( \hat{y} = 96.14x - 2.75; 1343 \) B. \( \hat{y} = -2.75x + 96.14; 58 \) C. \( \hat{y} = -2.75x - 96.14; 134.64 \) D. \( \hat{y} = -96.14x + 2.75; 1343 \) ### Explanation: - **Option B** is highlighted, suggesting it may be the correct answer. - The equation provided in Option B implies that the final grade decreases by 2.75 points for each additional absence, with an intercept of 96.14. For the predicted grade when a student is absent 14 times: - Using the equation from Option B, substitute \( x = 14 \) to calculate the predicted grade. ### Choose the correct option and calculate using the selected formula.
Transcribed Image Text:The data below represent the number of absences and the corresponding final grades of 9 randomly selected students from a literature class. The task is to find the least-squares regression line for the given data. Additionally, determine what the predicted final grade would be if a student was absent 14 times. The values for the least-squares regression line should be rounded to two decimal places, and the predicted grade should be rounded to the nearest whole number. ### Data: - **Number of absences, x:** 0, 3, 6, 4, 9, 2, 15, 8, 5 - **Final grade, y:** 98, 86, 80, 82, 71, 92, 55, 76, 82 ### Options for Least-Squares Regression Line: A. \( \hat{y} = 96.14x - 2.75; 1343 \) B. \( \hat{y} = -2.75x + 96.14; 58 \) C. \( \hat{y} = -2.75x - 96.14; 134.64 \) D. \( \hat{y} = -96.14x + 2.75; 1343 \) ### Explanation: - **Option B** is highlighted, suggesting it may be the correct answer. - The equation provided in Option B implies that the final grade decreases by 2.75 points for each additional absence, with an intercept of 96.14. For the predicted grade when a student is absent 14 times: - Using the equation from Option B, substitute \( x = 14 \) to calculate the predicted grade. ### Choose the correct option and calculate using the selected formula.
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