Least Squares Fitting_Polynomial

We are given measurements of air pollution, in the form of the concentration of NO (y=[110.49 73.72 23.39 17.11 20.31 29.37 74.74 117.02 298.04 348.13 294.75 253.78 250.48 239.48 236.52 245.04 286.74 304.78 288.76 247.11 216.73 185.78 171.19 171.73 164.05]), over a period of 24 hours(t=(0:24)), on a busy street in a major city. Since the NO concentration is mainly due to the cars, it has maximum values in the morning and in the afternoon, when the traffic is most intense. Here, we used the Least-Squares technique of data fitting for the purpose of
approximating measured discrete data: we fitted a polynomial to given data in order to be able to compute smoothed data for any value of the independent variable (t) in the model (f).