Step 1: Analyze the sequence. The sequence is given as: \[ x_n = \sum_{k=1}^{n} \frac{1}{\sqrt{k}} - 2(\sqrt{n} - 1). \] The term \( 2(\sqrt{n} - 1) \) is a term that grows like \( 2\sqrt{n} \) as \( n \) increases, and the sum \( \sum_{k=1}^{n} \frac{1}{\sqrt{k}} \) grows like \( 2\sqrt{n} \) for large \( n \).
Step 2: Behavior of the sequence for large \( n \). We approximate the sequence for large \( n \): \[ x_n \sim \sum_{k=1}^{n} \frac{1}{\sqrt{k}} - 2\sqrt{n}. \] Using the asymptotic approximation for the sum, we have: \[ \sum_{k=1}^{n} \frac{1}{\sqrt{k}} \sim 2\sqrt{n} - 1. \] Thus, \( x_n \) behaves like: \[ x_n \sim 2\sqrt{n} - 1 - 2\sqrt{n} = -1. \] This suggests that the sequence is bounded and converges to \( -1 \).
Step 3: Monotonicity. Given that \( x_n \) decreases as \( n \) increases, the sequence is monotonically decreasing. Thus, the correct answer is \( \boxed{(B)} \).
The equation of a closed curve in two-dimensional polar coordinates is given by \( r = \frac{2}{\sqrt{\pi}} (1 - \sin \theta) \). The area enclosed by the curve is ___________ (answer in integer).
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).