So in the population, there might be some true linear relationship. So in theory, on our x-axis, we would have our reaction time, and on our y-axis, you have your memory time. If you were able to plot every single possible data point, it might even be an infinite or near infinite, so it would be very hard to do it, but if there was just some truth in the universe that says, yes, there actually is a positive linear relationship, and it looks like this, and you could describe that regression line as y hat, it's a regression line, is equal to some true population parameter, which would be this y-intercept, so we could call that alpha, plus some true population parameter that would be the slope of this regression line, we could call that beta, times x. Now, we don't know what this truth of the universe is, of the linear relationship between reaction time and memory time, but we can try to estimate it, and that's what Jian is trying to do. So he's taking a sample of 24, so samples, samples, 24 data, data points, and that's much easier to then, you could even visualize it on a scatter plot like this, so you'd have one, two, three, four, five, six, seven, eight, nine, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24. You input those data points into a computer, and it does a regression line, and it's trying to minimize the squared distance to all of these points, and so let's say it gets a regression line that looks something like this, where this regression line can be described as some estimate of the true y-intercept, so this would actually be a statistic right over here that's estimating this parameter, plus some estimate of the true slope of the regression line, so this is just a statistic. This b is just a statistic that is trying to estimate the true parameter beta.