Ordinary Least Squares Problem

Hey guys, so I'm new to MATLAB and I got hung up on a particulat part of this problem part(c), if anyone has advice on any of the parts in this problem, that would be much appreaciated.

a. Create a row vector of 100 linearly spaced elements ranging from -10 to 10 and call it “x”.

b. Create a vector called y_original = m*x+b. (where: m = 2, b = -5)

c. Add white noise with an snr (signal-to-noise ratio) of 20 to the values of “y_original” and label this new vector “y_noise” (make sure y_original is not changed)

d. At this point, you now have realistic ‘x’ data and ‘y’ data. With this in mind, how could you re-arrange this data into a format such that you could use linear algebra to solve for the coefficients of the linear equation (m*x+b = y).

e. Set c = A\y. Make sure this is stored as a column vector.

f. What is the physical significance of these numbers?

g. Create a new vector called ‘y_predicted’ = A*c

h. Plot (x vs. y_noise) and (x vs. y_predicted).