Description: 
Function least_squares(x, y, m) fits a leastsquares polynomial of degree m through data points given in xy coordinates. the output to the function is a vector c = [c0, c1, ..., cm] whose components are the coefficients of the polynomial. The function can also be used in the following sense: least_squares(x, log(x), 1)  this will linearize data that obey an exponential curve y = b*exp(a*x) and the output c = [c0, c1] = [ln b, a]. Also, least_squares(x, 1./y, 1) linearizes data that follow a model y = 1/(ax + b). In other words, various combinations can be tested to linearize data (m=1) and from the graph display, we can see which combination linarizes data best
