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An instructor used 20 students to study the relationship between number of absences (x) and final grade (y) in a class. Use: x = 3.1, sx = 1.75, y = 83.4, sy = 7.03 and r=-0.861 to find the linear regression equation (linear correlation equation) y = a + bx for this data. Round your final answer to two decimal places. Linear Regression Equation: y = a + bx slope of the line: b = r. Sy Sx y-intercept of the line: a = ý - bx

An instructor used 20 students to study the relationship between number of absences (x) and final grade (y) in a class. Use: x = 3.1, sx = 1.75, y = 83.4, sy = 7.03 and r=-0.861 to find the linear regression equation (linear correlation equation) y = a + bx for this data. Round your final answer to two decimal places. Linear Regression Equation: y = a + bx slope of the line: b = r. Sy Sx y-intercept of the line: a = ý - bx

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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**Section 4.2** An instructor used 20 students to study the relationship between the number of absences (\(x\)) and final grade (\(y\)) in a class. Use: \(\bar{x} = 3.1\), \(s_x = 1.75\), \(\bar{y} = 83.4\), \(s_y = 7.03\) and \(r = -0.861\) to find the linear regression equation (linear correlation equation) \(y = a + bx\) for this data. **Round your final answer to two decimal places.** **Linear Regression Equation:** \(y = a + bx\) - **Slope of the line:** \(b = r \cdot \frac{s_y}{s_x}\) - **y-intercept of the line:** \(a = \bar{y} - b\bar{x}\)
Transcribed Image Text:**Section 4.2** An instructor used 20 students to study the relationship between the number of absences (\(x\)) and final grade (\(y\)) in a class. Use: \(\bar{x} = 3.1\), \(s_x = 1.75\), \(\bar{y} = 83.4\), \(s_y = 7.03\) and \(r = -0.861\) to find the linear regression equation (linear correlation equation) \(y = a + bx\) for this data. **Round your final answer to two decimal places.** **Linear Regression Equation:** \(y = a + bx\) - **Slope of the line:** \(b = r \cdot \frac{s_y}{s_x}\) - **y-intercept of the line:** \(a = \bar{y} - b\bar{x}\)
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