4- Consider the following data set representing the relationship between the independent variable x and the dependent variable y: x 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 y: 1.5 2.1 2.8 3.6 4.5 5.1 6.2 7.0 8.1 9.5 a. Use the method of linear least squares to derive the best-fit line y=mx+by for the given data. Calculate the slope m and the intercept b manually. Show all steps in your calculations, including how you compute the necessary sums (e.g., sums of x, y, x², xy). b. Write a MATLAB script to perform linear least squares regression on the same data set. Use the polyfit function to find the coefficients of the best-fit line and plot the data points along with the regression line.

Elementary Linear Algebra (MindTap Course List)
8th Edition
ISBN:9781305658004
Author:Ron Larson
Publisher:Ron Larson
Chapter5: Inner Product Spaces
Section5.CR: Review Exercises
Problem 62CR
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4-
Consider the following data set representing the relationship between the independent
variable x and the dependent variable y:
x 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0
y: 1.5 2.1 2.8 3.6 4.5 5.1 6.2 7.0 8.1 9.5
a. Use the method of linear least squares to derive the best-fit line y=mx+by for the given
data. Calculate the slope m and the intercept b manually. Show all steps in your
calculations, including how you compute the necessary sums (e.g., sums of x, y, x², xy).
b. Write a MATLAB script to perform linear least squares regression on the same data set.
Use the polyfit function to find the coefficients of the best-fit line and plot the data points
along with the regression line.
Transcribed Image Text:4- Consider the following data set representing the relationship between the independent variable x and the dependent variable y: x 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 y: 1.5 2.1 2.8 3.6 4.5 5.1 6.2 7.0 8.1 9.5 a. Use the method of linear least squares to derive the best-fit line y=mx+by for the given data. Calculate the slope m and the intercept b manually. Show all steps in your calculations, including how you compute the necessary sums (e.g., sums of x, y, x², xy). b. Write a MATLAB script to perform linear least squares regression on the same data set. Use the polyfit function to find the coefficients of the best-fit line and plot the data points along with the regression line.
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