An electricity utility company charges ₹7 per kWh. If a 40-watt desk light is left on for 10 hours each night for 180 days, what would be the cost of energy consumption? If the desk light is on for 2 more hours each night for the 180 days, what would be the percentage-increase in the cost of energy consumption?
First, convert the power rating to kilowatts:
\[ 40\ \text{W} = \frac{40}{1000} = 0.04\ \text{kW} \]
Case 1: Desk light used for 10 hours per day
\[ \text{Energy} = 0.04 \times 10 \times 180 = 72\ \text{kWh} \]
\[ \text{Cost} = 72 \times 7 = \u20B9504 \]
Case 2: Desk light used for 12 hours per day
\[ \text{Energy} = 0.04 \times 12 \times 180 = 86.4\ \text{kWh} \]
\[ \text{Cost} = 86.4 \times 7 = \u20B9604.8 \]
Percentage Increase:
\[ \frac{604.8 - 504}{504} \times 100 = \frac{100.8}{504} \times 100 \approx 20\% \]
Therefore, the percentage increase in cost is 20% and the original cost is ₹504.
List-I | List-II |
---|---|
(A) Confidence level | (I) Percentage of all possible samples that can be expected to include the true population parameter |
(B) Significance level | (III) The probability of making a wrong decision when the null hypothesis is true |
(C) Confidence interval | (II) Range that could be expected to contain the population parameter of interest |
(D) Standard error | (IV) The standard deviation of the sampling distribution of a statistic |
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).