So let's look at data for apartments in Queens, New York. Researchers want to use square footage to predict monthly rent. So, I want to first and foremost identify what is the Y variable and what is the X variable. Again, the X variable is going to be what we want to use. So in this case, we want to use square footage. So in this case, my X variable is going to be square footage, and the Y variable is what we want to predict. So what we want to predict is the monthly rent. So in this case, my Y variable is monthly rent, right? Again, response will come from whatever we want to predict, explanatory will come from what we want to use. And so, ultimately looking at my data, your X variable is going to be the square footage, your Y variable is going to be the monthly rent. Now, again, what I want to do is I want us to create a scatter plot. Now, what we have here in Orange is going to be the square footages for the X variable, and then the Y variable being the monthly rent. And I want you guys to look at this graph and tell me: does this scatter plot look linear or not linear? Yeah, it definitely looks linear. After creating the scatter plot, we can see here that the shape is linear, which means we can go about finding all right. So again, if we want to then compute r, step one is you need to enter in your data into your calculator. All right, you're going to need to enter into List 1 your X variable, so in this case, the square footage, and then into List 2 the monthly rents. Let's go to Stat, let's go to Edit, and let's go to Edit. You are going to need to clear out your List 1 and clear out your List 2. Now, please keep in mind the fact that we said square footage is going to be your X variable. So, therefore, we are going to type into List 1 the square footage data. All right, so I want to make a point of that for all of you guys who are typing in your calculators right now, make sure that the square footage data in Orange is what goes in your List 1. I'll give you guys a moment to type in your List 1. Remember that we said monthly rent is the Y variable and that, remember, you'll always write your Y values into List 2. So, toggling over to List 2, I then want you to type in the corresponding rent. So, keep in mind that for the 500-foot square foot apartment, you are typing in a rent of $650. From there, we'll go to the Stat button, go to Calc, and go to Option 8, LinReg(a+bx). All right, so after you type this in, you go to Stat, go to Calc, go to LinReg(a+bx), and honestly, you shouldn't even need to rewrite List 1 and List 2 by this point because of the fact we're already emphasizing your X variable is going to be in List 1, your Y variable is in List 2. And if we hit Enter, we'll see that our r value here, our r value is going to be 0.909. And ultimately, why is it so important that we found this r value? Well, it's particularly so we can look at its strength. The whole point of this entire process is to ask, can I make a prediction? The whole point of this entire section is to make a prediction. But you can only make a prediction if your r value is indicating a strong relationship. So, can you guys tell me, is this R value indicating a strong relationship, strong or weak? Is this relationship strong or weak? Yeah, it's strong because the r value is so close to one. What that's emphasizing is a strong association, and again, a strong association means we can predict. And so, what does this ability to predict mean? It means I can then find the regression line to be able to make that prediction. And guys, if you go to your calculator, you'll see at the top of your screen, you have y = a + bx, where in particular notice the a value is given to you, it's -34.31. For the regression line, we would say predicted monthly rent is going to equal that a value of -34.31 plus that b value of 2.21, all being multiplied by the x value, which again, guys, we already identified, we identified that as square footage. And so, guys, what I want you to see, what I want you to see is that LinReg gave us three for the price of one. It gave us the r value, the a value, and the b value. And if we type this as y = -34.31 + 2.21x into our scatter plot, we're actually going to see it's going to fall very, very nicely. So, let me type that in for you guys. Let's type it in: y = -34.31 + 2.21x. Do you guys see it? Do you guys see how nicely that line just goes right in the center of all of those data points? And it is fantastic. And so, what we then can do is we then can use this equation to ultimately make a prediction. Again, what I want to emphasize is that we can make a prediction. Why? Because that strong association means we can predict. And so, again, how are we ultimately going to predict? Well, what we're going to do is that we are going to take our equation, all right? We're going to take that equation, small fit down, all right? We're going to take our equation and ultimately acknowledge the fact that we are given the square footage of 995 square feet. So, we are going to replace square footage with 995 ft and ultimately plug that in, plug that into the equation. I'll have you guys do the complete math and ultimately tell me: what monthly rent will we predict for this apartment with 995 square feet? I'll have you guys finish that off. Yeah, perfect. $2164.64. And so, what we would say then is that we predict a 995 square foot apartment will ultimately have a predicted rent of $2164.64.