Expected Value/Variance of a Discrete Random Variable

In summary, the conversation discusses selecting a random card from a deck multiple times and calculating the total number of aces selected and the value of Y based on that. The formula for calculating E(f(X)) is given, and the conversation concludes with the correct answer being found.
  • #1
KingCalc
5
0

Homework Statement


A card is drawn at random from an ordinary deck of 52 cards and its face value is noted, and then this card is returned to the deck. This procedure is done 4 times all together. Let [tex]X[/tex] be the total number of aces selected and [tex]Y = \cos(\pi X/2).[/tex]

[tex]E[Y] = ?[/tex]

Homework Equations


The Attempt at a Solution


Twenty attempts, still no right answer. I've come to the conclusion that I just missed the boat with this one.
 
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  • #2
So X is binomial(4,1/13), right? So you know its discrete probability function p(x) for x = 0 .. 4. And you have [itex]Y = \cos(\pi X/2)[/itex] which I will call f(X). Why not use the formula:

[tex]E(f(X)) = \sum_{x=0}^4 f(x)p(x)[/tex]
 
  • #3
I guess it's just me being dumb but I really don't get this, even with the formula put right out in front of me.

Edit: I thought it would make sense that I just sum up [tex]p(1)\times\cos(\pi 1/2)+p(2)\times\cos(\pi 2/2)+p(3)\times\cos(\pi 3/2)+p(4)\times\cos(\pi 4/2)[/tex]

... which (I think) would be...

[tex](4/52)(48/52)(48/52)(48/52)\times\cos(\pi 1/2)+(4/52)(4/52)(48/52)(48/52)\times\cos(\pi 2/2)+(4/52)(4/52)(4/52)(48/52)\times\cos(\pi 3/2)+(4/52)(4/52)(4/52)(4/52)\times\cos(\pi 4/2)[/tex]

... but I guess I'm wrong?
 
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  • #4
KingCalc said:
I guess it's just me being dumb but I really don't get this, even with the formula put right out in front of me.

Am I correct in assuming that you do know the formula for p(x) given that X is a binomial random variable?

p(x) = ?

And the formula for f(x) is given to you. So calculate f(0)p(0) up through f(4)p(4) and add them up.
 
  • #5
Okay I got the answer, even though I don't understand why it works, but I guess I can find that out later. Thanks for the help.
 

Related to Expected Value/Variance of a Discrete Random Variable

1. What is expected value in statistics?

The expected value of a discrete random variable is the theoretical average of the possible outcomes of an experiment, weighted by their respective probabilities. It represents the long-term average of a random variable over many repeated trials.

2. How is expected value calculated?

To calculate the expected value of a discrete random variable, we multiply each possible outcome by its respective probability, and then add all the products together. This can be represented mathematically as E(X) = ΣxP(X=x), where X is the random variable and P(X=x) is the probability of outcome x.

3. What is variance in statistics?

Variance is a measure of how spread out the possible outcomes of a random variable are from its expected value. It is calculated by taking the sum of the squared differences between each outcome and the expected value, weighted by their respective probabilities.

4. How is variance related to expected value?

Variance is related to expected value because it measures the variability of a random variable around its expected value. A higher variance indicates that the possible outcomes are more spread out, while a lower variance indicates that they are closer to the expected value.

5. What are the practical applications of expected value and variance?

Expected value and variance are important concepts in statistics and probability theory, and have many practical applications. They are used in decision-making, risk analysis, and financial modeling, among others. For example, in the stock market, expected value and variance are used to calculate the potential return and risk associated with different investments.

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