USE MATLAB PLEASE.

Microsoft. MicrosoftEdge 8 wekyb3d8bbwe/TempState/Downloads/ME201 6%20H 03%20S e Chrome isn't your default browser 2) [5pts] Give an analytic formula for parameters B and k as a function of the data points (x,y). iF1.N. HINT: use the linear fit expressions for a0 and al you have obtained in 1) Problem. 2- Generalize Least-squares-30pts- A data set (x.y), i N, is given by xlinspace (0,10,100) Y2 sin (x 0.5 pi) Assuming you didn't know how the data points were generated, you would like to approximate the data set by a sinusoidal function ) [10 pts] Using the generalized least-square matrix formulation derived in class, determine the wobes.fitpanmeters-amplitude A and phase O-so that the data can be fitted by the function .sin(x + ) (I) NAME YOUR CODE MyFitl m HINT: using trigonometry, show that this fit function y can be rewritten as a linear parameter fit y-al sin(x) +a2 costx) (17) 2) [5 pts] Plot on the same graph the data points and the best fit function (over a more finely sampled horizontal axis to have a smooth curve). Comment on the accuracy of the fit NAME YOUR CODE MyFit2,m 3) [15 pts] Consider now the data set is noisy. That is the data are now given by X linspace(0.10.100); Y -2 sin(X+0.5pi) B2 (rand(size(X))0.5) Repeat part I when noise amplitude B varies successively from 0 to 3 in 0.1 increment. Plot the variations of the percentage true error for the fit amplitude A and phase you computed for these increasing value of B. (use the true values of amplitude Atrue-2 and phase offset OtruetT2 from Part I to define the percentage true error) For what values of parameter B is the fit no longer close to original sinusoidal fit? why ? NAME YOUR CODE MyFit3.m 222 PM O Type here to search Microsoft. MicrosoftEdge 8 wekyb3d8bbwe/TempState/Downloads/ME201 6%20H 03%20S e Chrome isn't your default browser 2) [5pts] Give an analytic formula for parameters B and k as a function of the data points (x,y). iF1.N. HINT: use the linear fit expressions for a0 and al you have obtained in 1) Problem. 2- Generalize Least-squares-30pts- A data set (x.y), i N, is given by xlinspace (0,10,100) Y2 sin (x 0.5 pi) Assuming you didn't know how the data points were generated, you would like to approximate the data set by a sinusoidal function ) [10 pts] Using the generalized least-square matrix formulation derived in class, determine the wobes.fitpanmeters-amplitude A and phase O-so that the data can be fitted by the function .sin(x + ) (I) NAME YOUR CODE MyFitl m HINT: using trigonometry, show that this fit function y can be rewritten as a linear parameter fit y-al sin(x) +a2 costx) (17) 2) [5 pts] Plot on the same graph the data points and the best fit function (over a more finely sampled horizontal axis to have a smooth curve). Comment on the accuracy of the fit NAME YOUR CODE MyFit2,m 3) [15 pts] Consider now the data set is noisy. That is the data are now given by X linspace(0.10.100); Y -2 sin(X+0.5pi) B2 (rand(size(X))0.5) Repeat part I when noise amplitude B varies successively from 0 to 3 in 0.1 increment. Plot the variations of the percentage true error for the fit amplitude A and phase you computed for these increasing value of B. (use the true values of amplitude Atrue-2 and phase offset OtruetT2 from Part I to define the percentage true error) For what values of parameter B is the fit no longer close to original sinusoidal fit? why ? NAME YOUR CODE MyFit3.m 222 PM O Type here to search