Question

question 01) Let Xi , i = 1 , 2 , 3 , … , 10...

question 01) Let Xi , i = 1 , 2 , 3 , … , 10 be normally distributed independent random variables X i ∼ N ( μ , σ2 ). Determine the mean μ = ......................

and the standard deviation σ = ..................... (round to the third decimal place), if it is known that 1/10 ∑10i=1 Xi ∼ N ( 1.5 , 0.3 ).

need  μ and σ

Homework Answers

Answer #1

X i ∼ N ( μ , σ^2 ).

That is X i ∼ N ( 1.5 , 0.3 ).

Mean = 1.5

Standard deviation = answer.

Example to calculate mean and standard deviation from 10 random variables:

Sample : {1,2,3,4,5,6,7,8,9,10}

Mean = sum of Xi / total variables

Mean = 5.5

(formula used for sum : sum of 1st n natural number that 1+2+3.....+n = n(n+1)/2}

Standard deviation :

x(i) x(i) - mean (x(i)-mean)^2
1 -4.5 20.25
2 -3.5 12.25
3 -2.5 6.25
4 -1.5 2.25
5 -0.5 0.25
6 0.5 0.25
7 1.5 2.25
8 2.5 6.25
9 3.5 12.25
10 4.5 20.25
Sum = 82.5

Standard deviation :

Know the answer?
Your Answer:

Post as a guest

Your Name:

What's your source?

Earn Coins

Coins can be redeemed for fabulous gifts.

Not the answer you're looking for?
Ask your own homework help question
Similar Questions
a) Let Xi for i = 1,2,...n be random variables with E[Xi] = μi (not necessarily...
a) Let Xi for i = 1,2,...n be random variables with E[Xi] = μi (not necessarily independent). Show that E[∑ni =1 Xi] = [∑ni =1 μi]. Show from Definition b) Suppose that random variables Yi for i = 1, 2,...,n are independent and identically distributed withE[Yi] =γ(gamma) and Var[Yi] = σ2, Use part (a) to show that E[Ybar] =γ(gamma). (c) Suppose that random variables Yi for i = 1, 2,...,n are independent and identically distributed with E[Yi] =γ(gamma) and Var[Yi]...
Let Xi, i = 1, 2..., 48, be independent random variables that are uniformly distributed on...
Let Xi, i = 1, 2..., 48, be independent random variables that are uniformly distributed on the interval [-0.5, 0.5]. (a) Find the probability Pr(|X1|) < 0.05 (b) Find the approximate probability P (|Xbar| ≤ 0.05). (c) Determine an approximation of a such that P(Xbar ≤ a) = 0.15
   Let {Xi} be i.i.d. random variables with P(Xi=−1) = P(Xi= 1) = 1/2. Let Sn=...
   Let {Xi} be i.i.d. random variables with P(Xi=−1) = P(Xi= 1) = 1/2. Let Sn= 1 +X1+. . .+Xn be symmetric simple random walk with initial point S0 = 1. Find the probability that Sn eventually hits the point 0. Hint: Define the events A={Sn= 0 for some n} and for M >1, AM = {Sn hits 0 before hitting M}. Show that AM ↗ A.
Let X1, X2, . . ., Xn be independent, but not identically distributed, samples. All these...
Let X1, X2, . . ., Xn be independent, but not identically distributed, samples. All these Xi ’s are assumed to be normally distributed with Xi ∼ N(θci , σ^2 ), i = 1, 2, . . ., n, where θ is an unknown parameter, σ^2 is known, and ci ’s are some known constants (not all ci ’s are zero). We wish to estimate θ. (a) Write down the likelihood function, i.e., the joint density function of (X1, ....
Let xi = i for i = 1, 2, . . . , 17, and let...
Let xi = i for i = 1, 2, . . . , 17, and let the corresponding yi (i = 1, 2, . . . , 17) numbers be (in the order of indeces) 5, 15, 42, 57, 65, 68, 69, 83, 87, 98, 105, 108, 108, 108, 110, 112, 116. Calculate the least squares regression line for these data. Find 95% CI for the quantities α, β, and σ^2 in the previous problem.
Let X be normally distributed with the mean μ = 100 and some unknown standard deviation...
Let X be normally distributed with the mean μ = 100 and some unknown standard deviation σ. The variable Z = X − A σ is distributed according to the standard normal distribution. Enter the value for A =  . It is known that P ( 95 < X < 105 ) = 0.5. What is P ( − 5 σ < Z < 5 σ ) =  (enter decimal value). What is P ( Z < 5 σ ) =  (as a...
Let X1, X2 be two normal random variables each with population mean µ and population variance...
Let X1, X2 be two normal random variables each with population mean µ and population variance σ2. Let σ12 denote the covariance between X1 and X2 and let ¯ X denote the sample mean of X1 and X2. (a) List the condition that needs to be satisfied in order for ¯ X to be an unbiased estimate of µ. (b) [3] As carefully as you can, without skipping steps, show that both X1 and ¯ X are unbiased estimators of...
Let the dynamics Xi , i = 1, 2, . . . , d be independently...
Let the dynamics Xi , i = 1, 2, . . . , d be independently and identically distributed as Z ∼ N(0, 1). One approach for modeling the short-term interest rate rt at any time t is given by defining rt ∆= X2 1 + X2 2 + . . . + X2 d . (a) Describe the distribution of the continuous random variable rt. (b) Find the probability that rt ∈ (0, 0.02] if d = 3. (c)...
Let X i ~ Unif(0, 1) for 1 <= i <= n be IID (independent identically...
Let X i ~ Unif(0, 1) for 1 <= i <= n be IID (independent identically distributed) random variables. Let Y = max(X 1 , …, X n ). What is E(Y)?
Let X1, X2,... be a sequence of independent random variables distributed exponentially with mean 1. Suppose...
Let X1, X2,... be a sequence of independent random variables distributed exponentially with mean 1. Suppose that N is a random variable, independent of the Xi-s, that has a Poisson distribution with mean λ > 0. What is the expected value of X1 + X2 +···+ XN2? (A) N2 (B) λ + λ2 (C) λ2 (D) 1/λ2