Then go look for the output. You should see the output you requested in Step 3.
Walking through an example
To see how to run a straight-line regression and interpret the output, we use the following example
throughout the rest of this chapter.
Consider how blood pressure (BP) is related to body weight. It may be reasonable to suspect that
people who weigh more have higher BP. If you test this hypothesis on people and find that there really
is an association between weight and BP, you may want to quantify that relationship. Maybe you want
to say that every extra kilogram of weight tends to be associated with a certain amount of increased
BP. Even though you are testing an association, the reality is that you believe that as people weigh
more, it causes their BP to go up — not the other way around. So, you would characterize weight as
the independent variable (X), and BP as the dependent variable (Y). The following sections take you
through the steps of gathering data, creating a scatter plot, and interpreting the results.
Gathering the data
Suppose that you recruit a sample of 20 adults from a particular clinical population to participate in
your study (see Chapter 6 for more on sampling). You weigh them and measure their systolic BP (SBP)
as a measure of their BP. Table 16-1 shows a sample of weight and SBP data from 20 participants.
Weight is recorded in kilograms (kg), and SBP is recorded in the strange-sounding units of millimeters
of mercury (mmHg).
TABLE 16-1 Weight and Blood Pressure Data
Participant Study ID Body Weight (kg) SBP (mmHg)
1
74.4
109
2
85.1
114
3
78.3
94
4
77.2
109
5
63.8
104
6
77.9
132
7
78.9
127
8
60.9
98
9
75.6
126
10
74.5
126
11
82.2
116
12
99.8
121
13
78.0
111
14
71.8
116
15
90.2
115
16
105.4
133
17
100.4
128
18
80.9
128
19
81.8
105
20
109.0
127