This function uses an F-test to determine the likelihood that an observed improvement of a fit to data warrants the use of additional parameters.
>> [ p, Fstat, df1, df2 ] = ftest(n,np1,np2,chi1,chi2)
The function requires five inputs: n is the number of data to be fit. np1 & np2 are the numbers of free parameters used in the two models. chi1 and chi2 are sums of the squares of the misfits to the data for the two models.
Primary output is p. p is the probability (between 0 and 1) that the improvement of the fit is due to chance. Therefore a small value of p means a high confidence that the additional parameters are warranted.
Function written for (and examples may be found in):
Anderson, K.B., and Conder, J.A., 2011, Discussion of Multicyclic Hubbert modeling as a method for forecasting future petroleum production, Energy and Fuels, dx.doi.org/10.1021/ef1012648.