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To help you solve this question, we first need the data set mentioned in the problem (for the pulse rates of adult females and males). The steps for constructing the 95% confidence interval are as follows:

1. **Find the sample mean** (\(\bar{x}\)) and sample standard deviation (\(s\)) for both the female and male pulse rates.
2. **Calculate the standard error** of the mean using the formula:
   \[
   SE = \frac{s}{\sqrt{n}}
   \]
   where \(n\) is the sample size.
3. **Use the critical value** for a 95% confidence interval, which corresponds to \(z = 1.96\) for a normal distribution.
4. **Calculate the confidence interval** with the formula:
   \[
   \bar{x} \pm z \cdot SE
   \]
   This will give you the confidence interval for both females and males.
5. **Compare the intervals** to determine if they overlap or not, and use the options provided (A, B, C, D) to conclude based on the comparison.

If you have access to the data set (pulse rates for males and females), either through the image or another source, I can proceed to calculate the confidence intervals. Could you provide the data or clarify if it’s available?

Do you need more details on this process?

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Here are 5 related questions:
1. What is the difference between a confidence interval and a point estimate?
2. How does increasing the sample size affect the width of a confidence interval?
3. What assumptions do you make when calculating a confidence interval?
4. How do you interpret overlapping confidence intervals?
5. How would a 99% confidence interval differ from a 95% confidence interval?

**Tip:** Always check that your data meets the normality assumption or use a large sample size for reliable confidence interval estimates.

Construct a 95% confidence interval estimate of the mean pulse rate of adult females; then do the same for adult males. Compare the results.

Learn how to construct a 95% confidence interval for adult female and male pulse rates and compare the results. This problem involves calculating the sample mean, standard error, and using critical values to interpret overlapping intervals.

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