 Okay, so now let's try our multivariate regression using all three predictors, the energy balance model simulation, El Nino and the NAO. So we go to a regression model and we select, with the right click of the mouse here, we can select all three quantities at once. So we've got the EBM simulation, El Nino and the NAO and we're trying to predict temperature, we run the regression and we can see that we now explain nearly 80% of the variation in that temperature series. We went from just under 72% to now essentially 80% of the variation using those three predictors. That's about as good as you can expect to do in a simple multivariate regression of this sort to explain four-fifths of the total variation in the data. We can see that the autocorrelation coefficient is small. It's not going to be statistically significant. We don't have to worry about autocorrelation of the residuals, which is nice. So let's now go back to the plot settings and we're going to plot our model simulation result, our multivariate regression result that is, which includes the energy balance simulation, El Nino and NAO, those two internal factors. Scroll down to model output and there you can see it. The red curve is our statistical model based on the three predictors that we've used. The blue curve is the actual temperature series and we've explained a fairly impressive amount of variation in the data. We can see the effect of volcanic eruptions and some of the short-term coolings that are seen in the record and then a lot of the other inter-annual fluctuations are at least partly explained by the NAO and El Nino. If we like, we can recover the regression coefficients in our multivariate regression, the constant term, the term multiplying the energy balance model simulation, the term multiplying the El Nino series and the term multiplying the NAO series and that sum of terms is our statistical model and it does quite well in this particular case. Finally, we can take a look at the residuals, what's left over that wasn't explained by our multivariate regression and that's what's shown with the green curve. There's some variability of course that's left over that isn't explained by the factors we've considered but there isn't a whole lot of structure in that time series suggesting that the results of this multivariate regression are probably meaningful and are telling us something about the underlying factors that explain long-term variations and year-to-year variations and decadal variations in Northern Hemisphere land temperatures over time.