1, write a MATLAB function function [a, b, d, sd2, mol] = linls(x,y) that finds the coefficients for the least squares linear model y = ar + b for the data pairs {(n,y )). The function should also return the vector d oontaining the absolute deviation for each data point, sd2 the sum of the square of the deviations, and md the maximam deviation. The function should also output a single graph with the original data as points and the model as a smooth curve. The graph should have a legend title, and both axes labeled. 2. Write the below MATLAB functions that estimate the least squares parameters by first transforming the data to a linear model. Each program should call your linls.m function. Note in each function d, sd2, and md are each as in the above. Each function should also out put a single graph with the original data as points and the model as a smooth curve. The graph should have a legend, title, and both axes labeled (a) functiona, b, d, sd2, mdotpm(x,y) to estimate the least squares fit of the one term power model y br. The function should first exit if any a, or y, are nonpositive and appropriately alert the user (b) function a, b, d, sd2, md otem(x,y) to estimate the least squares fit of the one term expo nential modelybe. The function should first exit if any y are nonpositive and appropriately (c)function a, b, d, sd2, mdx,y) to estimate the least squares fit of the saturation program should first exit if any of the , or y, are zero and -ar-. This appropriately alert the user