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Chapter 7
When Data Throws You a Curve:
Using Nonlinear Regression
In This Chapter
Determining when a straight-line regression model isn’t enough
Fitting a polynomial to your data set
Exploring exponential models to fit your data
n introductory statistics, you concentrate on the simple linear regression
Imodel, where you look for one quantitative variable, x, that you can use to
make a good estimate of another quantitative variable, y. The examples you
look at fell right in line with this kind of model, such as using height to esti-
mate weight or using GPA to estimate exam score. (For information on simple
linear regression models, see Chapter 4.)
Nonlinear regression comes into play in situations where you have graphed
your data on a scatterplot (a two dimensional graph showing the x variable
on the x-axis and the y variable on the y-axis), and you see a pattern emerging
that doesn’t look like a straight line, but instead looks like some type of curve.
Examples of data that follow a curve include population sizes over time,
demand for a product as a function of supply, or the length of time that a bat-
tery lasts. When a data set follows a curved pattern, the time has come to
move away from the linear regression models (Chapters 4 and 5) and move
on to a nonlinear regression model.

In this chapter, you see how to make your way around the curved road of
data that leads to nonlinear regression models. The good news is that you
can use many of the same techniques you use for regular regression and that
Minitab, in the end, does the analysis for you.