Midterm Exam Paper - Applied Probability | STAT 331, Exams of Probability and Statistics

Material Type: Exam; Class: APPLIED PROBABILITY; Subject: Statistics; University: Rice University; Term: Unknown 1989;

Typology: Exams

Pre 2010

Uploaded on 08/18/2009

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Stat 331/Elec 331, Midterm Exam, due October 9, at 5 pm
Solutions should be clear, complete and easy to follow. You are allowed to
to use the book, lecture notes and previous homework with solutions. Col-
laboration is not allowed. The time limit is five hours. The maximum score
is given at the end of each problem. Late turn-ins are not accepted.
1. The function fis defined as f(x) = cx2,0< x < 1 (and 0 otherwise).
a. Determine the constant cso that this becomes a pdf for a continuous
random variable X.
b. Compute E[X], V ar[X] and P(X > 0.5).
c. Let Y=Xand find the pdf for Y.
d. Compute E[Y] and V ar[Y]. (4)
2. Are the following claims true or false? Give proofs or counterexamples.
a. If Ais an event such that P(A) = 0, then A=.
b. If pis the pmf for a discrete random variable then p(x)1 for all x.
c. If fis the pdf for a continuous random variable then f(x)1 for all x.
d. If Xexp(λ), then 2Xexp (λ/2)
e. If Xunif (1,1), then |X| unif (0,1).
f. If Xbin (n, p), then 2Xbin (2n, p). (6)
3. A transmittor sends 0’s and 1’s to a receiver. Each digit is received cor-
rectly (0 as 0, 1 as 1) with probability 0.90. Digits are received correctly
independently of each other and on the average twice as many 0’s as 1’s are
1
pf2

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Stat 331/Elec 331, Midterm Exam, due October 9, at 5 pm

Solutions should be clear, complete and easy to follow. You are allowed to to use the book, lecture notes and previous homework with solutions. Col- laboration is not allowed. The time limit is five hours. The maximum score is given at the end of each problem. Late turn-ins are not accepted.

  1. The function f is defined as f (x) = cx^2 , 0 < x < 1 (and 0 otherwise).

a. Determine the constant c so that this becomes a pdf for a continuous random variable X.

b. Compute E[X], V ar[X] and P (X > 0 .5).

c. Let Y =

X and find the pdf for Y.

d. Compute E[Y ] and V ar[Y ]. ( 4 )

  1. Are the following claims true or false? Give proofs or counterexamples.

a. If A is an event such that P (A) = 0, then A = ∅.

b. If p is the pmf for a discrete random variable then p(x) ≤ 1 for all x.

c. If f is the pdf for a continuous random variable then f (x) ≤ 1 for all x.

d. If X ∼ exp(λ), then 2X ∼ exp (λ/2)

e. If X ∼ unif (− 1 , 1), then |X| ∼ unif (0, 1).

f. If X ∼ bin (n, p), then 2X ∼ bin (2n, p). ( 6 )

  1. A transmittor sends 0’s and 1’s to a receiver. Each digit is received cor- rectly (0 as 0, 1 as 1) with probability 0.90. Digits are received correctly independently of each other and on the average twice as many 0’s as 1’s are

being sent.

a. If 1 is received, what is the probability that 1 was sent?

b. If the sequence 10 is sent, what is the probability that it is received incorrectly?

c. If the sequence 10 is received, what is the probability that this is the sequence that was sent? ( 4 )

  1. Two friends have agreed to meet at 12.30. Assume that their arrival times are independent random variables, one uniformly distributed between 12.30 and 1.00 and the other uniformly distributed between 12.30 and 1.15. Compute the probability that the one who arrives first must wait more than ten minutes. ( 4 )
  2. Let X have a uniform distribution on (0, 1) and given that X = x, let the conditional distribution of Y be uniform on (0, 1 /x).

a. Find the joint pdf f (x, y) and sketch the region where it is positive.

b. Compute P (X > Y ).

c. Find fY (y), the marginal pdf of Y , and sketch its graph. ( 6 )