When converting a dataset to z-scores using the formula:

$z = \frac{x - \mu}{\sigma}$

where $\mu$ is the mean and $\sigma$ is the standard deviation, the mean of the z-scores will always be 0, and the standard deviation will always be 1. This transformation standardizes the dataset, centering it at 0 and scaling it to have a standard deviation of 1.

Thus, after converting all pulse rates of women to z-scores:

• $\mu = 0$
• $\sigma = 1$

Would you like a detailed explanation or have any questions?

Here are five related questions:

1. What is the significance of converting a dataset to z-scores?
2. How would you interpret a z-score of 1.5 in this context?
3. What happens to the shape of the distribution when data is converted to z-scores?
4. Can z-scores be used to identify outliers? If so, how?
5. How would you standardize a dataset that does not follow a normal distribution?

Tip: Z-scores are useful for comparing data points from different distributions because they convert values to a common scale.