© John Wiley & Sons, Inc.

FIGURE 16-1: Straight-line regression is appropriate for both strong and weak linear relationships (a and b), but not for nonlinear

(curved-line) relationships (c and d).

You should proceed with straight-line regression when one or more of the following are true:

You want to test whether there’s a statistically significant association between the X and Y

variables.

You want to know the value of the slope and/or intercept (also referred to as the Y intercept) of a

line fitted through the X and Y data points.

You want to be able to predict the value of Y if you know the value of X.

Understanding the Basics of Straight-Line

Regression

The formula of a straight line can be written like this:

. This formula breaks down

this way:

Y is the dependent variable (or outcome).

X is the independent variable (or predictor).