Babies Weights (Example 2) Some sources report that the weights of full-term newborn babies have a mean of 7 pounds and a standard deviation of 0.6 pound and are Normally distributed. a. What is the probability that one newborn baby will have a weight within 0.6 pound of the mean − that is, between 6.4 and 7.6 pounds, or within one standard deviation of the mean? b. What is the probability the average of four babies’ weights will be within 0.6 pound of the mean − that is, between 6.4 and 7.6 pounds? c. Explain the difference between a and b.
Babies Weights (Example 2) Some sources report that the weights of full-term newborn babies have a mean of 7 pounds and a standard deviation of 0.6 pound and are Normally distributed. a. What is the probability that one newborn baby will have a weight within 0.6 pound of the mean − that is, between 6.4 and 7.6 pounds, or within one standard deviation of the mean? b. What is the probability the average of four babies’ weights will be within 0.6 pound of the mean − that is, between 6.4 and 7.6 pounds? c. Explain the difference between a and b.
Babies Weights (Example 2) Some sources report that the weights of full-term newborn babies have a mean of 7 pounds and a standard deviation of 0.6 pound and are Normally distributed.
a. What is the probability that one newborn baby will have a weight within
0.6
pound of the
mean
−
that
is, between
6.4
and
7.6
pounds, or within one standard deviation of the mean?
b. What is the probability the average of four babies’ weights will be within
0.6
pound of the
mean
−
that
is, between
6.4
and
7.6
pounds?
c. Explain the difference between a and b.
Features Features Normal distribution is characterized by two parameters, mean (µ) and standard deviation (σ). When graphed, the mean represents the center of the bell curve and the graph is perfectly symmetric about the center. The mean, median, and mode are all equal for a normal distribution. The standard deviation measures the data's spread from the center. The higher the standard deviation, the more the data is spread out and the flatter the bell curve looks. Variance is another commonly used measure of the spread of the distribution and is equal to the square of the standard deviation.
A random variable X takes values 0 and 1 with probabilities q and p, respectively, with q+p=1. find the moment generating function of X and show that all the moments about the origin equal p. (Note- Please include as much detailed solution/steps in the solution to understand, Thank you!)
1 (Expected Shortfall)
Suppose the price of an asset Pt follows a normal random walk, i.e., Pt =
Po+r₁ + ... + rt with r₁, r2,... being IID N(μ, o²).
Po+r1+.
⚫ Suppose the VaR of rt is VaRq(rt) at level q, find the VaR of the price
in T days, i.e., VaRq(Pt – Pt–T).
-
• If ESq(rt) = A, find ES₁(Pt – Pt–T).
2 (Normal Distribution)
Let rt be a log return. Suppose that r₁, 2, ... are IID N(0.06, 0.47).
What is the distribution of rt (4) = rt + rt-1 + rt-2 + rt-3?
What is P(rt (4) < 2)?
What is the covariance between r2(2) = 1 + 12 and 13(2) = r² + 13?
• What is the conditional distribution of r₁(3) = rt + rt-1 + rt-2 given
rt-2 = 0.6?
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