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Answer :
To determine which issue can be addressed using the sample body temperatures provided, let's evaluate each option given the data:
We have temperatures at two different times (8 AM and 12 AM) for five subjects. The data table looks like this:
- At 8 AM: 97.0, 98.5, 97.6, 97.7, 98.7
- At 12 AM: 97.6, 97.8, 98.0, 98.4, 98.4
Now, let's go through each option:
A. The data can be used to address the issue of whether there is a correlation between body temperatures at 8 AM and at 12 AM.
- With temperatures recorded for the same subjects at two times, we can analyze if there's a pattern or relationship between the readings at 8 AM and 12 AM. This means we can check for a correlation, making this option a valid question for statistical analysis.
B. The data can be used to find the percentage of people whose body temperature increases with illness.
- Unfortunately, we don't have any additional information about the health status of the subjects in the sample. Without knowing if they are ill or have a baseline (normal) temperature, this analysis cannot be performed with the current data.
C. The data can be used to address the issue of whether there is a correlation between average body temperature and a person's exposure to sunlight.
- While interesting, this option is not supported by the table since we do not have any data regarding the subjects' exposure to sunlight. There is no basis for analyzing sunlight exposure with the given temperatures.
D. The data can be used to address the issue of whether there is a difference between average body temperature for males and for females.
- Again, we lack necessary information: the table doesn't specify the subjects' genders. Therefore, we cannot address this issue with the provided data.
Given the above explanations, the most suitable option we can address using statistical analysis of this data is:
A. The data can be used to address the issue of whether there is a correlation between body temperatures at 8 AM and at 12 AM.
We have temperatures at two different times (8 AM and 12 AM) for five subjects. The data table looks like this:
- At 8 AM: 97.0, 98.5, 97.6, 97.7, 98.7
- At 12 AM: 97.6, 97.8, 98.0, 98.4, 98.4
Now, let's go through each option:
A. The data can be used to address the issue of whether there is a correlation between body temperatures at 8 AM and at 12 AM.
- With temperatures recorded for the same subjects at two times, we can analyze if there's a pattern or relationship between the readings at 8 AM and 12 AM. This means we can check for a correlation, making this option a valid question for statistical analysis.
B. The data can be used to find the percentage of people whose body temperature increases with illness.
- Unfortunately, we don't have any additional information about the health status of the subjects in the sample. Without knowing if they are ill or have a baseline (normal) temperature, this analysis cannot be performed with the current data.
C. The data can be used to address the issue of whether there is a correlation between average body temperature and a person's exposure to sunlight.
- While interesting, this option is not supported by the table since we do not have any data regarding the subjects' exposure to sunlight. There is no basis for analyzing sunlight exposure with the given temperatures.
D. The data can be used to address the issue of whether there is a difference between average body temperature for males and for females.
- Again, we lack necessary information: the table doesn't specify the subjects' genders. Therefore, we cannot address this issue with the provided data.
Given the above explanations, the most suitable option we can address using statistical analysis of this data is:
A. The data can be used to address the issue of whether there is a correlation between body temperatures at 8 AM and at 12 AM.
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