>> Suppose we look in the classified section of a newspaper and we're looking at the price of automobiles and we're sort of shopping for an automobile of a certain type. So we go to that part of the newspaper and we check the age of this particular type of automobile against the price that is being asked for this type of automobile. Let's suppose we come up with this kind of information. Suppose this kind of automobile that's 5 years old has a price of \$8000.00. Now in this column I'm writing the price in thousands of dollars and over here is the age of this type of automobile. So an automobile that's 7 years old has a price of 5.7 times 1000, \$5700.00 would be the price of that car that's 7 years old. So I have data here that and the information is paired. So I have age of automobiles, price of those automobiles. Now, this paired data idea can lead us to a diagram or a graph involving this. If we just think of these as the first item of an ordered pair and this is the second item of an ordered pair, we have XY pairs here that we can place onto a graph. And placing this information onto a graph, I get this kind of graph. Now this graph is called a scatter diagram, often also called a scatter plot. And scatter plots can print many different kinds of patterns. But in this chapter, we're going to be investigating situations where we have a scatter plot and if that scatter plot has a pattern that is roughly linear, then we would like to calculate the best fitting line that correlates with the data. Now here I have a line that I can move around. Now a best fitting line would be a line that just kind of goes right in the middle of the information. We have about as many items, data points, above the line as below the line and they are about the same distance above and below the line. Certainly a line up here would not be a best fitting line. We want the line to sort of emulate the data that is given.