Final answer:
The proportion of people in a sample reporting 'good' health can be determined through statistical analysis using functions like tally(). The proportion is calculated by dividing the number of 'good' health responses by the total survey sample size. Hypothesis testing can compare sample proportions to national averages to assess representativeness.
Step-by-step explanation:
When attempting to determine the proportion of people in a sample who report being in good health, we use statistical inference tools such as the tally() function to analyze survey data. This situation often involves creating null and alternative hypotheses to test an assumed proportion against observed data.
In the context provided, participants may have been asked about their health status, leading to possible responses like 'Excellent,' 'Very Good,' 'Good,' 'Fair,' and 'Poor.' To find the specific proportion that reports being in 'good' health, one would need to tally the responses and divide the number of 'good' responses by the total number of responses.
For example, if a survey done by the CDC through the BRFSS (Behavioral Risk Factor Surveillance System) asked 100 nurses about their health status and 30 responded 'good,' the proportion would be 0.30 or 30%. Hypothesis testing, such as comparing survey results to national averages, helps researchers infer if a sample is representative or deviates significantly regarding health status, disease prevalence, or other measures.