Calculate sums:
\[ \sum f_i = 22, \quad \sum f_i x_i = 176, \quad \sum f_i x_i^2 = 2048. \]
Calculate the mean \( \bar{x} \):
\[ \bar{x} = \frac{\sum f_i x_i}{\sum f_i} = \frac{176}{22} = 8. \]
Calculate the variance \( \sigma^2 \):
\[ \sigma^2 = \frac{1}{N} \sum f_i x_i^2 - \bar{x}^2, \]
where \( N = \sum f_i \).
Plugging in values:
\[ \sigma^2 = \frac{1}{22} \times 2048 - (8)^2 = \frac{2048}{22} - 64. \]
Compute:
\[ \frac{2048}{22} = 93.09090909 \quad \text{and} \quad \sigma^2 = 93.09090909 - 64 = 29.09090909. \]
Thus, the variance is: 29.09090909
Let $x_1, x_2, \ldots, x_{10}$ be ten observations such that
\[\sum_{i=1}^{10} (x_i - 2) = 30, \quad \sum_{i=1}^{10} (x_i - \beta)^2 = 98, \quad \beta > 2\]
and their variance is $\frac{4}{5}$. If $\mu$ and $\sigma^2$ are respectively the mean and the variance of
\[ 2(x_1 - 1) + 4\beta, 2(x_2 - 1) + 4\beta, \ldots, 2(x_{10} - 1) + 4\beta\]
then $\frac{\beta \mu}{\sigma^2}$ is equal to:
For a statistical data \( x_1, x_2, \dots, x_{10} \) of 10 values, a student obtained the mean as 5.5 and \[ \sum_{i=1}^{10} x_i^2 = 371. \] He later found that he had noted two values in the data incorrectly as 4 and 5, instead of the correct values 6 and 8, respectively.
The variance of the corrected data is:
Let \( \{ W(t) : t \geq 0 \} \) be a standard Brownian motion. Then \[ E\left( (W(2) + W(3))^2 \right) \] equals _______ (answer in integer).