Let \( T: \mathbb{R}^3 \to \mathbb{R}^3 \) be a linear map defined by \[ T(x_1, x_2, x_3) = (3x_1 + 5x_2 + x_3, x_3, 2x_1 + 2x_3). \] {Then the rank of \( T \) is equal to ________ (answer in integer).}
To find the rank of the linear map \( T \), we need to determine the number of linearly independent rows in the matrix representation of \( T \). The map \( T \) is given by the following transformation: \[ T(x_1, x_2, x_3) = \begin{pmatrix} 3 & 5 & 1 \\ 0 & 0 & 1 \\ 2 & 0 & 2 \end{pmatrix} \begin{pmatrix} x_1 \\ x_2 \\ x_3 \end{pmatrix}. \] Thus, the matrix representation of \( T \) is: \[ A = \begin{pmatrix} 3 & 5 & 1 \\ 0 & 0 & 1 \\ 2 & 0 & 2 \end{pmatrix}. \] Now, we find the rank of this matrix by reducing it to row echelon form (REF). First, use row operations to simplify the matrix: Subtract \( \frac{2}{3} \) of the first row from the third row to make the element in the third row, first column, zero: \[ \begin{pmatrix} 3 & 5 & 1 \\ 0 & 0 & 1 \\ 0 & -\frac{10}{3} & \frac{4}{3} \end{pmatrix}. \] Multiply the third row by \( -\frac{3}{10} \) to make the second column entry of the third row equal to 1: \[ \begin{pmatrix} 3 & 5 & 1 \\ 0 & 0 & 1 \\ 0 & 1 & -\frac{2}{5} \end{pmatrix}. \] Finally, we can easily see that the first and second rows are linearly independent, and the third row is also linearly independent from the others. Thus, the matrix has 3 non-zero rows, so the rank of \( T \) is 3.
Rank of \( T \) is: 3
Let \( (X, Y)^T \) follow a bivariate normal distribution with \[ E(X) = 2, \, E(Y) = 3, \, {Var}(X) = 16, \, {Var}(Y) = 25, \, {Cov}(X, Y) = 14. \] Then \[ 2\pi \left( \Pr(X>2, Y>3) - \frac{1}{4} \right) \] equals _________ (rounded off to two decimal places).
Let \( X_1, X_2 \) be a random sample from a population having probability density function
\[ f_{\theta}(x) = \begin{cases} e^{(x-\theta)} & \text{if } -\infty < x \leq \theta, \\ 0 & \text{otherwise}, \end{cases} \] where \( \theta \in \mathbb{R} \) is an unknown parameter. Consider testing \( H_0: \theta \geq 0 \) against \( H_1: \theta < 0 \) at level \( \alpha = 0.09 \). Let \( \beta(\theta) \) denote the power function of a uniformly most powerful test. Then \( \beta(\log_e 0.36) \) equals ________ (rounded off to two decimal places).
Let \( X_1, X_2, \dots, X_7 \) be a random sample from a population having the probability density function \[ f(x) = \frac{1}{2} \lambda^3 x^2 e^{-\lambda x}, \quad x>0, \] where \( \lambda>0 \) is an unknown parameter. Let \( \hat{\lambda} \) be the maximum likelihood estimator of \( \lambda \), and \( E(\hat{\lambda} - \lambda) = \alpha \lambda \) be the corresponding bias, where \( \alpha \) is a real constant. Then the value of \( \frac{1}{\alpha} \) equals __________ (answer in integer).