Least-Squares Lines and Making Predictions

When we examine a set of bivariate data, remember that we first examine the data and its graph for the strength of the relationship between the two variables. We do this by calculating correlation coefficient between the two sets of data. That is, we want to see how well the dependent variable correlates with the independent variable.

What is the ultimate aim of analysing bivariate data?

Why do we bother to examine the strength of the relationship between two variables? Usually because we want to use this information to formulate predictions.

For example, we might want to study the effectiveness of spending money on Facebook advertising to attract new clients. We might collect data on a business similar to ours and then use our findings to predict how many clients we could expect to sign up for a monthly advertising spend of $500.

The Least Squares Regression Line

The first thing we need to do in order to use our data to make predictions, if to fit some sort of function to the shape of our data. For our purposes, we'll be looking at scattergraphs whose pattern is linear and we will therefore fit a linear function to our data.

You might remember as drawing in the Line of Best Fit. The more mathematically correct name is the Least Squares Regression Line, but effectively they're the same thing.

So what is the Least Squares Regression Line?

The best way to understand it is through a demonstration. 

Experiment with this Geogebra applet.

1. Refresh the applet and generate a new scattergraph to experiment with.
2. Drag the slider to Stage 2 and move the blue dots around the rectangle until you have a line which you think is the Line of Best Fit.
3. Drag the slider to Stage 3 and you'll see the residuals. For now, think of these as the distance between your line and the actual data.
4. Drag the slider to Stage 4. Here you see the residuals turn to squares. Now move the blue dots on the line around again and try to make the total area of all the squares combined to be as small as possible.
5. Drag the slider to Stage 5. How did you do? Your blue Line of Best Fit should be very close to the green Least Squares Regression Line. And the sum of your squares should be very close to the least sum of the squares.

Calculating the Least Squares Regression Line

Most of the time, you will be required to calculate the equation of the Least Squares Regression Line using technology.

Here's a great video on how to use the TI-Nspire to create a scatter graph and calculate the equation of the Least Squares Regression Line.

Making Predictions

Once we have our line, we're ready to start making predictions. Since our line is the best possible fit for the data we have, we can use it as a model to predict the likely value for the dependent variable based on a value for the independent variable that we'd like to predict for.

The process of predicting is two-fold.

• Firstly we need to substitute the $x$x value we are interested in, into our Least Squares Regression Line
• Then we need to consider whether our prediction is reliable or not

Making a Prediction using Technology

Let's watch this next video and see how we can make a prediction once we've input our data and calculated the Least Squares Regression Line.

Analysing the validity of our prediction

After you have used your linear model to make a prediction for a particular value for the independent variable, you now need to determine how valid or reliable it is.

To do this, you need to consider two things:

1. How strong is the correlation?
2. Is the data interpolated or extrapolated?

We already know all about correlation and the strength of a relationship. Let's talk about the second point.

Interpolation means you have used an $x$x value in your prediction that is within the available range of data that you were working with. In the video example, the $x$x values range between 35 and 98, so any x value you choose within this range would be considered an interpolation.

Extrapolation means you have used an $x$x value in your prediction that is outside the available range of data. So in the video example, anything below 35 or above 98 would be considered an extrapolation.

Let's put it all together.

• If my correlation is strong and I have used interpolation, then my prediction will be reliable.
• If my correlation is moderate to weak and I have used interpolation, then my prediction is far less reliable.
• If my correlation is strong and I have used extrapolation, then my prediction will be far less reliable, especially if I extrapolate far beyond the range of available data.
• The worst of all is if I have a weak correlation and I extrapolate. There's little chance of this prediction being reliable.

Worked Examples

QUESTION 1

QUESTION 2

QUESTION 3