Remember, we took a sample of 20 folks here, and we calculated a statistic which is the slope of the regression line. Every time you do a different sample, you will likely get a different slope, and this slope is an estimate of some true parameter in the population. This would sometimes also be called the standard error of the slope of the least squares regression line. 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.