B1. (a) Let X ~ Bin(n, p). By using the formula EX = Σxpx(x), x Show that EX = np. You may use the binomial expansion, Hint: First show that m Σπ agm-k (a+b)m = Σ k=0 (m) (+-) - (2)² }) (b) Let X and Y be random variables, and let a be a constant. Starting from the definition of covariance, show that Cov(aX,Y) = a Cov(X, Y). (c) Let X and Y be Bernoulli ( ✓ ✓) random variables. Write down a table for the joint PMF of X and Y for which X and Y are uncorrelated.
B1. (a) Let X ~ Bin(n, p). By using the formula EX = Σxpx(x), x Show that EX = np. You may use the binomial expansion, Hint: First show that m Σπ agm-k (a+b)m = Σ k=0 (m) (+-) - (2)² }) (b) Let X and Y be random variables, and let a be a constant. Starting from the definition of covariance, show that Cov(aX,Y) = a Cov(X, Y). (c) Let X and Y be Bernoulli ( ✓ ✓) random variables. Write down a table for the joint PMF of X and Y for which X and Y are uncorrelated.
Algebra & Trigonometry with Analytic Geometry
13th Edition
ISBN:9781133382119
Author:Swokowski
Publisher:Swokowski
Chapter10: Sequences, Series, And Probability
Section10.5: The Binomial Theorem
Problem 54E
Question

Transcribed Image Text:B1.
(a) Let X
~
Bin(n, p). By using the formula
EX = Σxpx(x),
x
Show that EX
= np.
You may use the binomial expansion,
Hint: First show that
m
Σπ agm-k
(a+b)m = Σ
k=0
(m)
(+-) - (2)²
})
(b) Let X and Y be random variables, and let a be a constant. Starting from the definition of
covariance, show that Cov(aX,Y) = a Cov(X, Y).
(c) Let X and Y be Bernoulli ( ✓ ✓) random variables. Write down a table for the joint PMF of X and Y
for which X and Y are uncorrelated.
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