We are tasked with finding the \( z \)-scores for specific points relative to the mean of a normal distribution with a standard deviation of \(\sigma = 20\). The \( z \)-score is calculated using the formula:
\[
z = \frac{{X - \mu}}{\sigma}
\]
where \( X \) is the value for which we are calculating the \( z \)-score, \(\mu\) is the mean of the distribution, and \(\sigma\) is the standard deviation.
For a point 10 points below the mean, we have:
- \( X = -10 \)
- \(\mu = 0\) (since we are considering deviations from the mean)
- \(\sigma = 20\)
Substituting these values into the formula:
\[
z = \frac{{-10 - 0}}{20} = -0.5
\]
For a point 30 points below the mean, we have:
- \( X = -30 \)
- \(\mu = 0\)
- \(\sigma = 20\)
Substituting these values into the formula:
\[
z = \frac{{-30 - 0}}{20} = -1.5
\]
For a point 5 points above the mean, we have:
- \( X = 5 \)
- \(\mu = 0\)
- \(\sigma = 20\)
Substituting these values into the formula:
\[
z = \frac{{5 - 0}}{20} = 0.25
\]
- The \( z \)-score for 10 points below the mean is \(\boxed{-0.5}\).
- The \( z \)-score for 30 points below the mean is \(\boxed{-1.5}\).
- The \( z \)-score for 5 points above the mean is \(\boxed{0.25}\).