To find the mean \( \mu \) of the data, we use the formula:
\[
\mu = \frac{\sum_{i=1}^N x_i}{N}
\]
For the given data, the calculation is:
\[
\mu = \frac{101}{10} = 10.1
\]
Next, we calculate the variance \( \sigma^2 \) using the formula for sample variance:
\[
\sigma^2 = \frac{\sum (x_i - \mu)^2}{n-1}
\]
After performing the calculations, we find:
\[
\sigma^2 = 3.66
\]
The standard deviation \( \sigma \) is the square root of the variance:
\[
\sigma = \sqrt{3.66} = 1.91
\]
Finally, we calculate the standard error (SE) of the sample mean using the formula:
\[
SE = \frac{\sigma}{\sqrt{n}}
\]
Where \( n \) is the number of observations. Thus, we have:
\[
SE = \frac{1.91}{\sqrt{10}} \approx 0.6
\]