solve using matlab

pData Local/Packages/Microsoft MicrosoftEdge Bwekyb3d8bbwe/Tem pState/Downloads/ME2016%20Hw03%20Sp Set as default Problem. 2- Generalize Least-squares-30pts- A data set (x;'yi), i=1.N, is given by xlinspace (0,10,100)i Y 2*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. 1) [10 pts] Using the generalized least-square matrix formulation derived in class, determine the two best.fit parameters-amplitude A and phase O- so at the data can be fitted by the function y A sin(x +e) (1) 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 cos(x) (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+0.5*pi)+ B*2"(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 do you computed for these increasing values of B. (use the true values of amplitude Atrue-2 and phase offset Otrue /2 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