 Hi, this is Dr. Don and I have a problem out of Chapter 2. This is about correlation and trends and the problem talks about an analyst who is collecting average driving distance in yards of golfers and their accuracy, the percent of drives that land in a fairway, of the 20 best golfers at the club. There's a professional there who's trying to increase his driving distance, but he's concerned that his accuracy will be lower. Does the data support his concern that as he increases his driving length that his accuracy will go down? This is a pretty straightforward problem. They want you to construct a scatter plot. There's a typo there. It says all 20 cities. It's all 20 golfers. But if we click on the icon, we'll get the data to open up, and then we will click on the little rectangle there to copy the table, and we want to open it in StatCrunch. When you get StatCrunch open, you can quickly generate the scatter plot by going to Graph, ScatterPlot, and then the first decision you have to make, what is my X variable? What's on the X axis and what's on the Y variable? Well, you think through the problem. Here, the golfers concern that as distance increases, his accuracy will go down. So you can think of the X variable as the predictor. That's the things he can control. As he increases his distance of driving, will the accuracy go down? Will accuracy be the response? The response is the Y variable, the predictor variable, or the thing he controlled is always the X variable. So here, we would have driving distance would be the X, and accuracy would be the Y. So we can leave everything else just the default and click Compute, and we'll get a plot here. I'm going over to the side, and I'm going to close this to get it out of the way. And the question is, which of these plots here resemble this plot? Now you can sometimes just look at the plot that StatCrunch gives you and make a visual comparison there. Sometimes it's kind of tricky. This plot and this plot are very similar. You have to look very closely to see which of the two is the better. One thing you might consider, and it's not that critical on this problem, but look at the limits on the Y axis and the X axis on these three samples. It goes from 50 to 75, from 290 to 320. The default in StatCrunch doesn't give you those limits. They're different. So sometimes you can make it easier to tell by changing the minimum and the maximum value on the X and Y. Here our minimum needs to be 290 on the X, and the max is 320. I'm going to click OK, go back this little icon there. This time click Y axis. Our minimum is 50, and our max is 75, and click OK. And now we are lined up, so we should be able to tell that that looks pretty much like that. So I would choose A as the matching scatter plot. The second part is his concern, the golfer's concern that as the distance increases does accuracy go down. And just looking at that you can say, yeah, generally there's a trend that as the driving distance increases, accuracy goes down. Now one thing you can do is click on Options, Edit, and we're going to overlay a line that don't be scared, the polynomial in this case is just a single, which would be a good old fashioned linear straight line, best fit line, trend line. And click Compute, and it will overlay a trend line there, and you can see it's definitely down and to the right as driving distance increases, accuracy goes down.