We can calculate the slope that we got for our sample regression line, minus the slope we're assuming in our null hypothesis, which is going to be equal to zero, so we know what we're assuming, and we can calculate the standard error of the sampling distribution. In fact, our computer has already done it for us, and this is an estimate of this, and we know what number that is, so we know what all of these numbers are, but if you're using an estimate of the standard deviation of the sampling distribution, and we've seen this before when we've done inferential statistics using for means, it is appropriate to use a t statistic, but with that said, pause the video. What is this going to be equal to? Well, this is going to be equal to the slope for our sample regression line. We know it's 14.686, minus our assumed true population parameter, the slope of the true regression line. Well, we're assuming that is zero, so minus zero, and then we divide that by the standard error, which is going to be, we could view this as a standard error for B, and so this is divided by 13.329, so it's just gonna be 14.686 divided by 13.329, and if we assume, if we're doing a one-sided test here, what we would then do is take this t statistic and think about the degrees of freedom, and then say, and then calculate a p value. What is the probability of getting a result at least this far above t is equal to zero, or what is the probability of getting a t statistic this high or higher, and that would be our p value, and if that's below some threshold, let's say, hey, that's pretty unlikely, then we would reject the null, and that which would suggest the alternative, but they're not asking us to do all of that.