Name Section 4.2 Show all your work An instructor used 20 students to study the relationship between number of absences (x) and final grade (y) in a class. Use: = 3.1, sx = 1.75, y = 83.4, sy = 7.03 x Sx 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. la.) Linear Regression Equation: y = a + bx 1 b.) slope of the line: b = r. Sy Sx y-intercept of the line: a =ỹ - b*
Name Section 4.2 Show all your work An instructor used 20 students to study the relationship between number of absences (x) and final grade (y) in a class. Use: = 3.1, sx = 1.75, y = 83.4, sy = 7.03 x Sx 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. la.) Linear Regression Equation: y = a + bx 1 b.) slope of the line: b = r. Sy Sx y-intercept of the line: a =ỹ - b*
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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please answer the question 1a,b&c and show work!! thank you also please make sure if writing on paper handwriting can be read thank you again :)
![### 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 the following data:
- \(\bar{x} = 3.1\)
- \(s_x = 1.75\)
- \(\bar{y} = 83.4\)
- \(s_y = 7.03\)
- \(r = -0.861\)
to find the linear regression equation (linear correlation equation) \(y = a + bx\) for this data. Round your final answer to two decimal places.
1. **Linear Regression Equation**: \(y = a + bx\)
2. **Slope of the line**:
\[
b = r \cdot \frac{s_y}{s_x}
\]
3. **y-intercept of the line**:
\[
a = \bar{y} - b\bar{x}
\]
This setup will help calculate the linear regression line that can predict final grades based on the number of absences.](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2F60a89b3d-a9e0-44dc-91ec-a2e89fd95064%2F91de26f2-fcc6-4b77-ab24-f04bb088343e%2Fhwmna4i_processed.jpeg&w=3840&q=75)
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 the following data:
- \(\bar{x} = 3.1\)
- \(s_x = 1.75\)
- \(\bar{y} = 83.4\)
- \(s_y = 7.03\)
- \(r = -0.861\)
to find the linear regression equation (linear correlation equation) \(y = a + bx\) for this data. Round your final answer to two decimal places.
1. **Linear Regression Equation**: \(y = a + bx\)
2. **Slope of the line**:
\[
b = r \cdot \frac{s_y}{s_x}
\]
3. **y-intercept of the line**:
\[
a = \bar{y} - b\bar{x}
\]
This setup will help calculate the linear regression line that can predict final grades based on the number of absences.
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