 Now, let's look at the residuals or what's left over in the data when we remove this regression line. We can use the regression model tool here to find the residuals. Regression we've done is where our independent variable is year, and our target variable, our dependent variable is temperature. We can run that regression here, and that gives us two things. First of all, it tells us the autocorrelation in the residuals, which as we can see is pretty small. This value down here. It's minus 0.11. And if we look up the statistical significance of a negative correlation of 0.11 on the order of 107 degrees of freedom, we'll find that it's statistically insignificant. So that means in this particular case, it doesn't look like we have to worry about the added caveats associated with autocorrelation when our residuals do not look like uncorrelated white noise, but instead have this low frequency structure. Now we can actually plot these residuals, and I'll make a plot here. We'll go back to plot settings. I go down to model residuals, and so I'm going to plot the residuals as a function of year. I no longer need a trend line here, but it will keep a zero line, and that's what we have. So when we remove the trend, we count it for a statistically significant trend, and when we remove that trend, this is what was left over. These are the residuals, and they look pretty much like Gaussian random white noise, which is good. That means that the results for our regression are basically sound. We fulfilled the basic underlying assumption that what's left over after we account for the significant trend in the data looks random.