Refer to the following study and the SAS output:
Aerobic fitness is measured by the ability to consume oxygen. Collecting oxygen consumption measurements data to investigate aerobic fitness can be expensive and cumbersome. A study was conducted at North Carolina State University to examine which measurements from simple exercise tests could be used to predict aerobic fitness. The sample consisted of 31 men involved in a physical fitness course at North Carolina State University. The oxygen intake rate (ml per kg body weight per minute) was measured while the men were running. The other variables collected were age (years), weight (kg), time to run 1.5 miles (minutes), heart rate while resting (beats per minute, bpm), heart rate while running (bpm), and maximum heart rate recorded while running (bpm). It is important to note that the maximum heart rate was highly correlated with the heart rate while running, so the model presented here includes only one of these variables (the latter) to avoid multicollinearity issues. Using a 0.05 significance level, we need to determine whether aerobic fitness (oxygen intake rate) can be predicted from these simple exercise test measurements.
Analysis of Variance Sum of Squares Mean Source DF Square F Value Pr> F Model Error Corrected Total 139.21044 6.21317 30 851.
What type of analysis is this?
a. Linear regression
b. One-way ANOVA
c. Two-way ANOVA
d. ANCOVA
e. Logistic regression
How many model degrees of freedom are there in this problem?
a. 1
b. 5
c. 6
d. 24
e. 25
What percentage of the variation in oxygen intake rate can be explained by the linear association between oxygen intake rate and the collection of variables given in this problem?
a. 61.0%
b. 66.8%
c. 78.1%
d. 88.4%
e. 90.4%
What is the best-predicted oxygen intake rate of a 48-year-old man weighing 77.5 kg with a resting heart rate of 52 bpm who runs 1.5 miles in 10 minutes with a running heart rate of 170 bpm?
48.9
60.6
71.8
83.5
Cannot be determined from the given information
