Expectations of Random Variables

In summary: X|X|X = (2C1)(3C2) In summary, the expectation of the number of values that do not appear in the sample is 3.
  • #1
hbweb500
41
1
I am working on correcting an exam so that I may study for my probability final. Unfortunately, I don't have the correct answers, so I was hoping that someone here might be able to check my thought process.

1) Pick three numbers without replacement from the set {1,2,3,3,4,4,4}. Let T be the number of values that do not appear in your sample. Find the expectation of T.

I believe that to solve this one would use counting indicator variables. [tex]A_i[/tex] would indicate that at least one [tex]i[/tex] was drawn, such that [tex]4-T = A_1 + A_2 + A_3+A_4[/tex]. Expectation is linear, so [tex] E(T) = 4 - E(A_1) -E(A_2) - E(A_3)-E(A_4)[/tex]. The expectation of $A_i$ is just the probability of obtaining it, so [tex]E(t) = 4 - 3(1/7+1/7+2/7+3/7) = 1[/tex]

There are a couple of things that worry me. First, if we scale up the set so that in includes 100 elements: {1,2,3,3,4,4,4...,4,4}, then there are still 4 distinct values. I still choose three numbers at random. By the calculation above, I get 1 again, though I would expect it now to be closer to 3, since I have almost no chance of drawing a 1, 2, or 3. I think the fault lies in my calculation of the probabilities. I want the probability that I get *at least one* 1, at least one 2, and so one. I could do this by taking the complement: 1 minus the probability that I get no ones, no twos, etc, but this would be a complicated binomial problem, wouldn't it?
 
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  • #2
I've read through this, but can you explain "Let T be the number of values that do not appear in your sample"?

say C is your choice, do you mean if:
C = {4,4,4} then T = 3
C = {4,4,3} then T = 2 and so on?
 
  • #3
Yes, that is what I mean.

In any case, I thought about it, and the determination of the probabilities isn't as hard as I thought it would be. For example, the probability of getting at least one 2 is the complement of getting no twos.

The probability of getting no twos is:

[tex] \frac{{1 \choose 0} {6 \choose 3}}{{7 \choose 3}} [/tex]

Which is the standard calculation for sampling without replacement.
 
  • #4
i'm still thinking on it, but i think one bust might be the Ai are not purely independent events...

so its worth examining it from a case point of view & see if we can generalise from there... then we can pick the easiest cases to solve remembering the probs must all sum to 1.

T=4, clearly P(T=4) = 0

T=3
this can only happen if we choose 444,

T=2
this can happen with the following outcomes: 133, 144, 233, 244, 344, 334

T=1
that can happen if we draw: 123, 134, 234

T= 0, P(T=0) = 0, again clearly

so looking at the T=3 case, the number of the ways to draw 3 objects from 7 without any order is:
[tex] {}^7 C_{3} = \frac{7!}{3!4!} [/tex]
there is only distinct case where we choose all 3 4s, so the probabilty is:
[tex] Pr(T=3) = \frac{1}{{}^7 C_{3}}[/tex]
 
  • #5
then if you can do the probabilities for T=1, you've cracked it...
 
  • #6
another case based way is the X| method, X means we take a number, | means we skip to the next pile

as said, in total there are [itex] {}^7 C_{3} = \frac{7!}{3!4!} =7.5 = 35[/itex] distinct combinations we can end up with, when order is not important

cases are

T = 3
|||XXX = 1

T = 2
X||XX| = 1
X|||XX = 3C2
|X||XX|= 1
|X|||XX = 3C2
||XX|X = 3C1
||X|XX = (2C1)(3C2)

T = 1
X|X|X| = 2C1
X||X|X = (2C1)(3C1)
|X|X|X = (2C1)(3C1)
 

Related to Expectations of Random Variables

1. What is a random variable?

A random variable is a numerical quantity whose value is determined by chance. It represents an uncertain outcome of a random experiment or process.

2. How is a random variable different from a regular variable?

A regular variable has a fixed value, while a random variable can take on different values with varying probabilities. It is often denoted by capital letters such as X, Y, or Z.

3. What are the types of random variables?

There are two types of random variables: discrete and continuous. Discrete random variables can only take on a finite or countably infinite number of values, while continuous random variables can take on any value within a specific range.

4. How are expectations of random variables calculated?

The expectation of a random variable is calculated by multiplying each possible value of the random variable by its corresponding probability, and then adding all of these products together. This can also be expressed mathematically as the integral or sum of the product of the random variable and its probability distribution function.

5. What is the significance of expectations of random variables?

The expectation of a random variable is a key concept in probability theory and statistics. It represents the average or mean value that can be expected from a random experiment or process. It is also a fundamental tool for making predictions and analyzing data in various fields such as finance, economics, and engineering.

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