Linear Relationships – Linear Regression

• Summarizing the Pattern of the Data with a Line
• In General
• Let’s Summarize

Summarizing the Pattern of the Data with a Line

So far we’ve used the scatterplot to describe the relationship between two quantitative variables, and in the special case of a linear relationship, we have supplemented the scatterplot with the correlation (r).

The correlation, however, doesn’t fully characterize the linear relationship between two quantitative variables — it only measures the strength and direction. We often want to describe more precisely how one variable changes with the other (by “more precisely,” we mean more than just the direction), or predict the value of the response variable for a given value of the explanatory variable.

In order to be able to do that, we need to summarize the linear relationship with a line that best fits the linear pattern of the data. In the remainder of this section, we will introduce a way to find such a line, learn how to interpret it, and use it (cautiously) to make predictions.

Again, let’s start with a motivating example:

Earlier, we examined the linear relationship between the age of a driver and the maximum distance at which a highway sign was legible, using both a scatterplot and the correlation coefficient. Suppose a government agency wanted to predict the maximum distance at which the sign would be legible for 60-year-old drivers, and thus make sure that the sign could be used safely and effectively.

How would we make this prediction?

It would be useful if we could find a line (such as the one that is presented on the scatterplot) that represents the general pattern of the data, because then,

and predict that 60-year-old drivers could see the sign from a distance of just under 400 feet we would simply use this line to find the distance that corresponds to an age of 60 like this:

How and why did we pick this particular line (the one shown in red in the above walkthrough) to describe the dependence of the maximum distance at which a sign is legible upon the age of a driver? What line exactly did we choose? We will return to this example once we can answer that question with a bit more precision.

The technique that specifies the dependence of the response variable on the explanatory variable is called regression. When that dependence is linear (which is the case in our examples in this section), the technique is called linear regression. Linear regression is therefore the technique of finding the line that best fits the pattern of the linear relationship (or in other words, the line that best describes how the response variable linearly depends on the explanatory variable).

To understand how such a line is chosen, consider the following very simplified version of the age-distance example (we left just 6 of the drivers on the scatterplot):

There are many lines that look like they would be good candidates to be the line that best fits the data:

It is doubtful that everyone would select the same line in the plot above. We need to agree on what we mean by “best fits the data”; in other words, we need to agree on a criterion by which we would select this line. We want the line we choose to be close to the data points. In other words, whatever criterion we choose, it had better somehow take into account the vertical deviations of the data points from the line, which are marked with blue arrows in the plot below:

The most commonly used criterion is called the least squares criterion. This criterion says: Among all the lines that look good on your data, choose the one that has the smallest sum of squared vertical deviations. Visually, each squared deviation is represented by the area of one of the squares in the plot below. Therefore, we are looking for the line that will have the smallest total yellow area.

This line is called the least-squares regression line, and, as we’ll see, it fits the linear pattern of the data very well.

For the remainder of this lesson, you’ll need to feel comfortable with the algebra of a straight line. In particular you’ll need to be familiar with the slope and the intercept in the equation of a line, and their interpretation.

Like any other line, the equation of the least-squares regression line for summarizing the linear relationship between the response variable (Y) and the explanatory variable (X) has the form: Y = a + bX

All we need to do is calculate the intercept a, and the slope b, which we will learn to do using software.

Let’s go back now to our motivating example, in which we wanted to predict the maximum distance at which a sign is legible for a 60-year-old. Now that we have found the least squares regression line, this prediction becomes quite easy:

Comment About Predictions:

• Suppose a government agency wanted to design a sign appropriate for an even wider range of drivers than were present in the original study. They want to predict the maximum distance at which the sign would be legible for a 90-year-old. Using the least squares regression line again as our summary of the linear dependence of the distances upon the drivers’ ages, the agency predicts that 90-year-old drivers can see the sign at no more than 576 + (- 3 * 90) = 306 feet:

(The green segment of the line is the region of ages beyond 82, the age of the oldest individual in the study.)

Question: Is our prediction for 90-year-old drivers reliable?

Answer: Our original age data ranged from 18 (youngest driver) to 82 (oldest driver), and our regression line is therefore a summary of the linear relationship in that age range only. When we plug the value 90 into the regression line equation, we are assuming that the same linear relationship extends beyond the range of our age data (18-82) into the green segment. There is no justification for such an assumption. It might be the case that the vision of drivers older than 82 falls off more rapidly than it does for younger drivers. (i.e., the slope changes from -3 to something more negative). Our prediction for age = 90 is therefore not reliable.

In General

Prediction for ranges of the explanatory variable that are not in the data is called extrapolation. Since there is no way of knowing whether a relationship holds beyond the range of the explanatory variable in the data, extrapolation is not reliable, and should be avoided. In our example, like most others, extrapolation can lead to very poor or illogical predictions.

Let’s Summarize

• A special case of the relationship between two quantitative variables is the linear relationship. In this case, a straight line simply and adequately summarizes the relationship.

• When the scatterplot displays a linear relationship, we supplement it with the correlation coefficient (r), which measures the strength and direction of a linear relationship between two quantitative variables. The correlation ranges between -1 and 1. Values near -1 indicate a strong negative linear relationship, values near 0 indicate a weak linear relationship, and values near 1 indicate a strong positive linear relationship.

• The correlation is only an appropriate numerical measure for linear relationships, and is sensitive to outliers. Therefore, the correlation should only be used as a supplement to a scatterplot (after we look at the data).

• The most commonly used criterion for finding a line that summarizes the pattern of a linear relationship is “least squares.” The least squares regression line has the smallest sum of squared vertical deviations of the data points from the line.

• The slope of the least squares regression line can be interpreted as the estimated (or predicted) change in the mean (or average) value of the response variable when the explanatory variable increases by 1 unit.

• The intercept of the least squares regression line is the average value of the response variable when the explanatory variable is zero.  Thus, this is only of interest if it makes sense for the explanatory variable to be zero AND we have observed data in that range (explanatory variable around zero) in our sample.

• The least squares regression line predicts the value of the response variable for a given value of the explanatory variable. Extrapolation is prediction of values of the explanatory variable that fall outside the range of the data. Since there is no way of knowing whether a relationship holds beyond the range of the explanatory variable in the data, extrapolation is not reliable, and should be avoided.