The variance of a data set is the square of its standard deviation. Given that the standard deviation of the sample weights is:

$\text{Standard Deviation} = 12.9808 \, \text{kg}$

Formula for Variance:

$\text{Variance} = (\text{Standard Deviation})^2$

Now, let’s calculate:

$\text{Variance} = (12.9808)^2 = 168.501 \, \text{kg}^2$

Final Answer:

The variance of the sample data is:

$168.501 \, \text{kg}^2$

Let me know if you have any questions!

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Here are 5 related questions:

1. What is the relationship between standard deviation and variance in statistics?
2. Why is variance measured in squared units?
3. How does sample variance differ from population variance?
4. What effect does increasing the sample size have on the variance?
5. How can outliers affect the variance of a data set?

Tip: Always keep in mind that variance is more sensitive to outliers than standard deviation since it squares deviations.