Quiz Solutions for Math 183 Fall 2006 - Probability Theory, Quizzes of Data Analysis & Statistical Methods

The solutions to quiz 1 for the math 183 probability theory course offered in fall 2006. It covers various concepts such as expected value, variance, dependent and independent random variables, and conditional probability and expectation.

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Pre 2010

Uploaded on 03/28/2010

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Math 183
Fall 2006
Quiz 1 Solutions
1a. If we look at the wheel, we see that
P[Z= 1] = P[Z= 2] = P[Z= 3] = 1
6+1
12 +1
12 =1
3
So
E[Z] = 1 ·1
3+ 2 ·1
3+ 3 ·1
3= 2
1b. It is a theorem was proved in class that V ar[Z] = E[Z2](E[Z])2. We
have
E[Z2] = 12·1
3+ 22·1
3+ 32·1
3=14
3
So
V ar[Z] = 14
322=2
3
1c. There are two ways to do this problem. Since E[X+Y] = E[X] + E[Y],
we can compute these values separately, and then add them together. We can
also compute this value directly from the definition, which I do here; starting
from the top of the wheel and moving clockwise:
E[X+Y] = ((3) + (4)) 1
6+ ((3) + (5)) 1
6+ ((3) + (6))1
6
+ (2 + 1) 1
12 + (2 + 0) 1
12 + (2 + (1)) 1
12
+ (5 + 4) 1
12 + (5 + 3) 1
12 + (5 + 2) 1
12
=3
2
1d. Because Xand Zare independent, we have E[X×Z] = E[X]×E[Z]. So
1
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Math 183

Fall 2006

Quiz 1 Solutions

1a. If we look at the wheel, we see that

P [Z = 1] = P [Z = 2] = P [Z = 3] =

So

E[Z] = 1 ·

1b. It is a theorem was proved in class that V ar[Z] = E[Z^2 ] − (E[Z])^2. We have

E[Z^2 ] = 1^2 ·

+ 2^2 ·

+ 3^2 ·

So

V ar[Z] =

1c. There are two ways to do this problem. Since E[X + Y ] = E[X] + E[Y ], we can compute these values separately, and then add them together. We can also compute this value directly from the definition, which I do here; starting from the top of the wheel and moving clockwise:

E[X + Y ] = ((−3) + (−4))

1d. Because X and Z are independent, we have E[X × Z] = E[X] × E[Z]. So

we compute the value of E[X].

E[X] = (−3) ·

So

E[X × Z] = E[X]E[Z] =

1e. One random variable is dependent on another if the value of the first can always be determined from the value of the second. In the given wheel, if Z = 1, Y could be (−4), 1 or 4, so Y can not be dependent on Z. Also, if Z = 1, X could be (−3), 2 or 5, so X is not dependent on Z. Similarly, if X = −3, there are three possible values of Y and three possible values for Z, so neither Y nor Z depends on X. However, if Y = −4, we must have X = −3 and Z = 1. A similar statement can be made for any value of Y –the value of Y always determines the value of X and Z. So X and Z are both dependent on Y.

1f. A pair of random variables is independent if knowing the value of one of them does not affect the probabilities of the other. Since X and Z are dependent on Y , the pairs (X, Y ), (Y, Z) cannot be independent. To check the pair (X, Z), we first recall that P [Z = 1] = P [Z = 2] = P [Z = 3] = 13. Then we check that if X = −3, we have P [Z = 1] = P [Z = 2] = P [Z = 3] = 13. The same is true if X = 2 or if X = 5, so the pair (X, Z) is independent.

1g. From the definition of conditional probability we have

P [Y = 4 | Z = 1] =

P [Y = 4 and Z = 1] P [Z = 1]

=

1 12 1 3 =

1h. From the definition of conditional expectation we have

E[Y | Z = 1] = (−4) · P [Y = − 4 | Z = 1] + 1 · P [Y = 1 | Z = 1] + 4 · P [Y = 4 | Z = 1]

= (−4) ·

  1. In order to select a red ball from urn B, we could select a red ball from A and then a red ball from B, or we could select a white ball from A and then a red