To calculate the standard deviation, we need more information beyond just the mean. Specifically, the standard deviation requires the following:

1. The individual data points (or a distribution).
2. Alternatively, you can also calculate it if you have the variance.

The formula for standard deviation $\sigma$ is:

$\sigma = \sqrt{\frac{1}{N} \sum_{i=1}^{N} (x_i - \mu)^2}$

Where:

• $x_i$ are the individual data points.
• $\mu = 119.20$ is the mean.
• $N$ is the number of data points.

If you have the variance $\sigma^2$, the standard deviation is the square root of the variance.

Do you have the data points or the variance? This will allow me to provide a specific value for the standard deviation.

Would you like to clarify your question or ask for more information?

Here are some related questions:

1. How do you calculate variance given a set of data points?
2. What is the difference between population and sample standard deviation?
3. How does the standard deviation relate to the normal distribution?
4. What are the implications of a high versus low standard deviation?
5. How can you use standard deviation to identify outliers in data?

Tip: The standard deviation gives you a sense of how spread out the data is around the mean; a larger standard deviation indicates greater variability.