To complete these statements, we need to know the mean and standard deviation values for the normally distributed human pregnancy lengths. Let's denote:

• Mean: $\mu$
• Standard deviation: $\sigma$

The normal distribution follows the empirical rule, also known as the 68-95-99.7 rule, which helps in approximating how data falls within certain intervals around the mean:

1. 68% of data falls within 1 standard deviation from the mean, i.e., between $\mu - \sigma$ and $\mu + \sigma$.
2. 95% of data falls within 2 standard deviations from the mean, i.e., between $\mu - 2\sigma$ and $\mu + 2\sigma$.
3. 99.7% of data falls within 3 standard deviations from the mean, i.e., between $\mu - 3\sigma$ and $\mu + 3\sigma$.

Now, let's complete the statements:

(a) Approximately 68% of pregnancies have lengths between $\mu - \sigma$ and $\mu + \sigma$.

This statement corresponds to the 68% interval of the empirical rule.

(b) Approximately 95% of pregnancies have lengths between $\mu - 2\sigma$ and $\mu + 2\sigma$.

This statement follows the 95% interval of the empirical rule.

If you provide the mean and standard deviation values, I can give you exact numerical ranges for these intervals.

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Do you want further details or have any questions about this? Here are some related questions for further exploration:

1. What is the probability that a pregnancy lasts longer than $\mu + 2\sigma$ days?
2. How would the percentage change if the standard deviation increased?
3. How can we use the normal distribution to estimate the probability of extreme pregnancy lengths?
4. What is the z-score of a pregnancy length at $\mu + \sigma$?
5. How does the shape of the normal distribution affect predictions about pregnancy lengths?

Tip: In a normal distribution, probabilities can be easily computed using z-scores, which help compare individual data points relative to the mean.