Now, you could imagine, every time you take a different sample, you might get a different estimate of these things, and when we're doing inferential statistics, we set up hypotheses, you set up a null and an alternative hypothesis, and the null hypothesis is always the no news here, and no news, when you're dealing with regressions, is that even though you might suspect there's a positive linear relationship, even though you might see it in the data you got, it's, for your null hypothesis, you wanna assume that there is no positive linear relationship, so our null hypothesis here would be that the true slope of the true regression line, this, the parameter right over here, is equal to zero, so beta is equal to zero, so our null hypothesis is actually, might be that our true regression line might look something like this, that what y is is somewhat independent of what x is, and that if you suspect that there is a positive linear relationship, you could say something like, well, my alternative hypothesis is that my beta is greater than zero, or if you suspect that there's just some linear relationship, you don't know if it's positive or negative, then you might say that the beta is not equal to zero, but here it says he noticed, or he suspects, a positive linear relationship, so this would be his alternative hypothesis, but what you then want to do to test your null hypothesis, which we've done multiple, multiple times, is find a test statistic that is associated with the statistic for b that you actually got. Now, ideally, you would take your b, you would take your b, and from that, subtract the slope assumed in the null hypothesis, so the slope of the regression line you get, minus the slope that's assumed from the null hypothesis, and then divide by the standard deviation of the sampling distribution of the slope of the regression line, and if you did this, you would get a, it would be appropriate to use a z statistic over here. Now, the problem is is that we don't know exactly what the standard deviation of the sampling distribution is, but we can estimate it. 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.