def calcFstats(predTst, yTest, p, axis=<NUM_LIT:0>):
rss = np.sum((yTest - predTst) ** <NUM_LIT:2>, axis=axis)<EOL>tss = np.sum((yTest - yTest.mean()) ** <NUM_LIT:2>, axis=axis)<EOL>n = yTest.shape[<NUM_LIT:0>]<EOL>vecFvals = ((tss - rss)/p)/(rss/(n-p-<NUM_LIT:1>))<EOL>df1 = p - <NUM_LIT:1><EOL>df2 = n-<NUM_LIT:1><EOL>vecPvals = stats.f.cdf(vecFvals, df1, df2)<EOL>return vecFvals, vecPvals<EOL>
calculate coefficient of determination. Assumes that axis=0 is time

        Parameters
        ----------
        predTst : np.array, predicted reponse for yTest
        yTest : np.array, acxtually observed response for yTest
        p: float, number of predictors
        Returns
        -------
        aryFunc : np.array
            R2