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11.2 QUALITATIVE INDEPENDENT VARIABLES 539
TABLE 11.1.2
Data from a Random Sample of 15 Students
Case No. SBP Weight (lbs.) BMI
1 126 125 24.41
2 129 130 23.77
3 126 132 20.07
4 123 200 27.12
5 124 321 39.07
6 125 100 20.90
7 127 138 22.96
8 125 138 24.44
9 123 149 23.33
10 119 180 25.82
11 127 184 26.40
12 126 251 31.37
13 122 197 26.72
14 126 107 20.22
15 125 125 23.62
Correlations: SBP, Weight, BMI
SBP
Weight 0.289
p-value 0.296
Weight
BMI 0.213 0.962
p-value 0.447 0.000
FIGURE 11.1.2
Example 11.1.2.
Correlations calculated in MINITAB software for the data in
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11.2 QUALITATIVE INDEPENDENT VARIABLES
The independent variables considered in the discussion in Chapter 10 were all quantitative;
that is, they yielded numerical values that were either counts or measurements in
the usual sense of the word. For example, some of the independent variables used in our
examples and exercises were age, education level, collagen porosity, and collagen tensile
strength. Frequently, however, it is desirable to use one or more qualitative variables
as independent variables in the regression model. Qualitative variables, it will be recalled,
are those variables whose “values” are categories and that convey the concept of attribute
rather than amount or quantity. The variable marital status, for example, is a qualitative
variable whose categories are “single,” “married,” “widowed,” and “divorced.”
Other examples of qualitative variables include sex (male or female), diagnosis, race,
occupation, and immunity status to some disease. In certain situations an investigator