Probability and Statistics Problems from MATH 418 December 2008, Exams of Probability and Statistics

A series of probability and statistics problems from math 418 december 2008. The problems cover topics such as rare inherited genetic diseases, poisson distribution, symmetric simple random walks, conditional expectation, joint distribution, characteristic functions, and convergence in distribution. Students are asked to calculate probabilities, expectations, and find distributions.

Typology: Exams

2012/2013

Uploaded on 02/25/2013

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MATH 418 December 2008. Page 2 out of 8
1. [15/100] A rare inherited genetic disease behaves as follows. For people with the gene,
no symptoms arise at all with probability 1 p, and symptoms appear with probability p
at age 50. Charles is 49. His father Arthur (who is 80) and his sister Belinda (who is 52)
have no symptoms. However Arthur’s father Zoltan did have symptoms.
Because the disease is rare, you can neglect the possibility that Zoltan had 2 copies
of the gene, or that Arthur’s wife or mother had any copies of the gene. People without
the gene never exhibit symptoms.
What is the probability that Charles will exhibit symptoms next year?
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  1. [15/100] A rare inherited genetic disease behaves as follows. For people with the gene, no symptoms arise at all with probability 1 − p, and symptoms appear with probability p at age 50. Charles is 49. His father Arthur (who is 80) and his sister Belinda (who is 52) have no symptoms. However Arthur’s father Zoltan did have symptoms. Because the disease is rare, you can neglect the possibility that Zoltan had 2 copies of the gene, or that Arthur’s wife or mother had any copies of the gene. People without the gene never exhibit symptoms. What is the probability that Charles will exhibit symptoms next year?
  1. [13/100] Let Xi be i.i.d Ber(p) r.v., and N be Poisson with parameter λ and independent of the Xi. Set

S =

∑^ N

i=

Xi,

and let Y = N − S. (a) Calculate the joint distribution of S and Y.

(b) Find E(N |S).

  1. [15/100] (a) Let X and Y be jointly continuous random variables. Define E(X|Y ).

(b) Show that E

E(X|Y )

= E(X).

(c) Let X have an exponential distribution with mean λ. Let Y be a r.v. which has an exponential distribution with mean X. What is the joint p.d.f. of X, Y?

(d) Find E(Y ).

  1. [15/100] True or False. If True, give a brief proof. If False, provide a counter-example. (a) If events A, B are independent then A and Bc^ are independent.

(b) If Xn → C in probability, where C is a constant, then X^2 → C^2 in probability.

(c) If ϕ(t) is the characteristic function of a r.v. X then

|ϕ(t + h) − ϕ(t)| ≤ |ϕ(h) − 1 |

for all t ∈ R and |h| < 1.

  1. [14/100] An aircraft seats N = 400 passengers. Passengers have weights with mean 90 kg, and variance 300 kg^2. Passengers have no luggage with probability 1/2, and have luggage with weight 20 kg with probability 1/2. (All these may be assumed to be inde- pendent.) Let W be the total weight of passengers and baggage. The airline wants to find x such that P (W > x) = 0.01. Use the central limit theorem to find an approximation to x. If Φ(y) is the distribtion function of a N (0, 1) r.v. then Φ(2.33) ≃ 0 .99.