13656
This solution details the calculation for a summation expression, using the provided mean of observations.
We are given 20 observations (\(x_1, \dots, x_{20}\)) with a mean \(\bar{x}=1.414\). The mean formula is \( \bar{x} = \frac{\sum_{i=1}^{n} x_i}{n} \).
Using the given values (\(n=20\)):
\( \sum_{i=1}^{20} x_i = n \times \bar{x} = 20 \times 1.414 = 28.28 \)We need to find the value of \(\sum_{i=1}^{20} 100(2x_i + 4)\). We apply summation properties:
1. Factor out the constant 100:
\( 100 \sum_{i=1}^{20} (2x_i + 4) \)2. Split the summation and simplify:
\( 100 \left( \sum_{i=1}^{20} 2x_i + \sum_{i=1}^{20} 4 \right) \)3. Use the sum of \(x_i\) and the sum of a constant (\( \sum_{i=1}^{n} c = n \times c \)):
\( 100 \left( 2 \sum_{i=1}^{20} x_i + (20 \times 4) \right) \)4. Substitute the calculated sum \(\sum_{i=1}^{20} x_i = 28.28\) and calculate:
\( 100 \left( 2 \times 28.28 + 80 \right) \) \( = 100 \left( 56.56 + 80 \right) \) = 100 (136.56) = 13656The calculated value of the summation is 13656.
What is the median height of the class?
What is the total number of students whose height is less than or equal to 165 cm?
The height which occurs most frequently in the class is
Methods of presentation of data are:
Consider 35 i.i.d. observations X1, X2, ...., X15 and Y1, Y2, ..., Y20. Let R be the Wilcoxon's rank sum statistic based on the ranks of the X' s in the combined sample. Then the expected value of R is
Let I, J > 5. Consider two-way ANOVA, where the observations satisfy the linear model
yij = α + βi + γj + εij, 1 ≤ i ≤ I, 1 ≤ j ≤ J
E(εij) = 0, Var(εij) = σ2, \(\sum_{i=1}^{!} β_i=\sum_{j=1}^j γ_j=0 .\)
In this set - up
Let X1,X2,...,Xn be a random sample from exponential distribution with mean θ. Which of the following is NOT a sufficient statistic for θ?
A newly developed algorithm for random number generation needs to be tested. The first step is to check whether the sequence of numbers generated can be considered a random sample from the uniform distribution on the interval (0, 1). Which of the following is an appropriate nonparametric test?