Biostatistics

596 CHAPTER 11 REGRESSION ANALYSIS: SOME ADDITIONAL TECHNIQUES
Age
(years)
BMI
PaO 2
(mm Hg)
PaCO 2
(mm Hg)
FEV 1
(% Predicted)
Lowest
Ex.
Sao 2
a
Mean
Sleep
Sao 2
a
Lowest
Sleep
Sao 2
a
Fall
Sleep
Sao 2
a
63 26.12 51.75 46.8 39 67 69.31 46 34.9
62 21.71 72 41.1 27 88 87.95 72 22
67 24.75 84.75 40.575 45 87 92.95 90 2.17
57 25.98 84.75 40.05 35 94 93.4 86 8.45
66 32.00 51.75 53.175 30 83 80.17 71 16
a Treated as dependent variable in the authors’ analyses. BMI ¼ body mass index; Pao 2 ¼ arterial oxygen
tension: Paco 2 ¼ arterial carbon dioxide pressure; FEV 1 ¼ forced expiratory volume in 1 second; Sao 2 ¼ arterial
oxygen saturation.
Source: Data provided courtesy of Dr. Eithne Mulloy.
Exercises for Use with the Large Data Sets Available on the Following Website:
www.wi ley.com/ college/dan iel
1. The goal of a study by Gyurcsik et al. (A-27) was to examine the usefulness of aquatic exerciserelated
goals, task self-efficacy, and scheduling self-efficacy for predicting aquatic exercise attendance
by individuals with arthritis. The researchers collected data on 142 subjects participating in
Arthritis Foundation Aquatics Programs. The outcome variable was the percentage of sessions
attended over an 8-week period (ATTEND). The following predictor variables are all centered values.
Thus, for each participant, the mean for all participants is subtracted from the individual score. The
variables are:
GOALDIFF—higher values indicate setting goals of higher participation.
GOALSPEC—higher values indicate higher specificity of goals related to aquatic exercise.
INTER—interaction of GOALDIFF and GOALSPEC.
TSE—higher values indicate participants’ confidence in their abilities to attend aquatic classes.
SSE—higher values indicate participants’ confidence in their abilities to perform eight tasks related
to scheduling exercise into their daily routine for 8 weeks.
MONTHS—months of participation in aquatic exercise prior to start of study.
With the data set AQUATICS, perform a multiple regression to predict ATTEND with each of the
above variables. What is the multiple correlation coefficient? What variables are significant in
predicting ATTEND? What are your conclusions?
2. Rodehorst (A-28) conducted a prospective study of 212 rural elementary school teachers. The
main outcome variable was the teachers’ intent to manage children demonstrating symptoms of
asthma in their classrooms. This variable was measured with a single-item question that used a
seven-point Likert scale (INTENT, with possible responses of 1 ¼ extremely probable to 7 ¼
extremely improbable). Rodehorst used the following variables as independent variables to predict
INTENT:
SS ¼ Social Support. Scores range from 7 to 49, with higher scores indicating higher perceived
social support for managing children with asthma in a school setting.
ATT ¼ Attitude. Scores range from 15 to 90, with higher scores indicating more favorable attitudes
toward asthma.