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Chapter 4: Getting in Line with Simple Linear Regression 81

Think about it: If the relationship you found actually continued for any value
of x, no matter how large, then a 250-pound lineman from OSU would have to
carry 3.69 + 0.113 * 250 = 31.94 pounds of books around in his backpack. Of
course this would be easy for him, but what about the rest of us?

Sometimes you need more

than one variable

A simple linear regression model is just what it says it is: simple. I don’t mean
easy to work with, necessarily, but simple in the uncluttered sense. The
model tries to estimate the value of y by only using one variable, x. However,
the number of real-world situations that can be explained by using a simple,
one-variable linear regression is small. Often one variable just can’t do all the
predicting.

If one variable alone doesn’t result in a model that fits well enough, you can
try to add more variables. It may take many variables to make a good esti-
mate for y, and you have to be careful in how you choose them. In the case of
stock market prices, for example, they’re still looking for that ultimate predic-
tion model.

As another example, health insurance companies try to estimate how long
you’ll live by asking you a series of questions (each of which represents a
variable in the regression model). You can’t find one single variable that
estimates how long you’ll live; you must consider many factors: your health,
your weight, whether or not you smoke, genetic factors, how much exercise
you do each week, and the list goes on and on and on.

The point is that regression models don’t always use just one variable, x,
to estimate y. Some models use two, three, or even more variables to esti-
mate y. Those models aren’t called simple linear regression models; they’re
called multiple linear regression models because of their employment of mul-
tiple variables to make an estimate. (You explore multiple linear regression
models in Chapter 5.)

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