Now these last two columns you don't have to worry about in the context of this video. This is useful if you were saying, well, assuming that there is no relationship between caffeine intake and time studying, what is the associated t statistics for the statistics that I actually calculated, and what would be the probability of getting something that extreme or more extreme, assuming that there is no association, assuming that, for example, the actual slope of the regression line is zero, and this says, well, the probability if we would assume that is actually quite low. It's about a 1% chance that you would have gotten these results if there truly was not a relationship between caffeine intake and time studying. But with all of that out of the way, let's actually answer the question. Well, to construct a confidence interval around a statistic, you would take the value of the statistic that you calculated from your sample, so 0.164, and then it would be plus or minus a critical t value, and then this would be driven by the fact that you care about a 95% confidence interval and by the degrees of freedom, and I'll talk about that in a second, and then you would multiply that times the standard error of the statistic, and in this case, the statistic that we care about is the slope, and so this is 0.057 times 0.057, and the reason why we're using a critical t value instead of a critical z value is because our standard error of the statistic is an estimate. We don't actually know the standard deviation of the sampling distribution. So the last thing we have to do is figure out what is this critical t value?