Step 1: Understanding the Median
The median of a random variable \( X \) is the value \( m \) such that \( F(m) = 0.5 \). We are given that the median is \( \frac{1}{\sqrt{2}} \), so we need to solve for \( \alpha \) when \( F\left( \frac{1}{\sqrt{2}} \right) = 0.5 \).
Step 2: Using the Distribution Function
From the given distribution function, for \( 0 \leq x < 1 \), the CDF is: \[ F(x) = \alpha(1 + 2x^2). \] We substitute the median value \( x = \frac{1}{\sqrt{2}} \) into this expression: \[ F\left( \frac{1}{\sqrt{2}} \right) = \alpha \left( 1 + 2 \left( \frac{1}{\sqrt{2}} \right)^2 \right). \] Simplifying the expression: \[ F\left( \frac{1}{\sqrt{2}} \right) = \alpha \left( 1 + 2 \times \frac{1}{2} \right) = \alpha(2). \] We are told that the median is \( \frac{1}{\sqrt{2}} \), so: \[ F\left( \frac{1}{\sqrt{2}} \right) = 0.5. \] Thus, we have: \[ \alpha \times 2 = 0.5 \quad \Rightarrow \quad \alpha = \frac{0.5}{2} = \frac{1}{4}. \] Thus, the value of \( \alpha \) is \( \boxed{\frac{1}{4}} \).
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).