 Okay, so continuing where we left off, the red curve is showing us the component of the variation in December state college temperatures that can be explained by El Nino. If in a particularly strong El Nino year where the Nino 3.4 index is say as large as plus two, we get a December temperature that's about a degree and a half Fahrenheit above average. That is to say twice that 0.74 degrees effect that we get for a one unit change in Nino 3.4 for a particularly strong La Nino event which would correspond to a negative Nino 3.4 anomaly of say negative two or so, we would get a one and a half degree Fahrenheit cooling effect on state college December temperatures. So the influence of El Nino is small compared to the overall variability in the series, but it is statistically significant at least if we are able to motivate one-sided hypothesis test. If we had reason or priority to believe that El Nino events warm state college temperatures in the winter, then the regression gives us a result that's significant at the .05 level, the standard threshold for statistical significance. Okay, so it may not be that satisfying, we're not explaining a large amount of variation in the data, but we do appear to be explaining a statistically significant fraction of variability in the data. Now, finally, let's look at the trend, sorry, let's look at the residuals from that regression. And what I'll do is I will get rid of these graphs that we have right now, and I'm just going to plot the model residuals as a function of time. That's what they look like. There isn't a whole lot of obvious structure, and in fact, if we go back to the regression model tab, and we look at the value of the lag 1 autocorrelation coefficient, we see that is minus .09, that's quite, it's slightly negative, it's quite small, close to zero. If we look up the statistical significance, it's not going to be even remotely significant, so we don't have to worry about autocorrelation influencing our estimate of statistical significance. We also don't have much evidence here of the sort of low frequency structure in the residuals that might cause us worry. So the nominal results of our regression analysis appear valid. And again, if we were to invoke a one-sided hypothesis test, we would have found a statistically significant, albeit a weak, influence of El Nino on state college December temperatures.