To find the mean \( \mu \) of the dataset, we use the formula:
\[ \mu = \frac{\sum_{i=1}^N x_i}{N} \]
For the given data \( 124, 36, 26, 133, 76, 73, 115 \), the sum is \( 583 \) and the number of data points \( N \) is \( 7 \). Thus, we have:
\[ \mu = \frac{583}{7} = 83.29 \]
The variance \( \sigma^2 \) is calculated using the formula:
\[ \sigma^2 = \frac{\sum (x_i - \mu)^2}{N} \]
Substituting the mean \( \mu = 83.29 \) into the formula, we compute the squared differences and their sum, which results in a variance of:
\[ \sigma^2 = 1544.49 \]
The population standard deviation \( \sigma \) is the square root of the variance:
\[ \sigma = \sqrt{\sigma^2} = \sqrt{1544.49} = 39.3 \]
\(\boxed{39.30}\)
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