Expected values for random variables

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  • #1
cue928
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0
I am stuck on the following problem: Five items are to be sampled from a large lot of samples. The inspector doesn't know that three of the five sampled items are defective. They will be tested in randomly selected order until a defective item is found, at which point the entire lot is rejected. Y is the number of firing pins the inspector must test. Find, graph the probability distribution of Y.

I understand that p(1) = 3/5. But it is saying that p(2) =3/10 and p(3)=1/10, I do not see how they are calculating that.
 
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  • #2
So p(2) is the probability that the second sample tested is defective. That means that you are looking at the case where the first one is non-defective (otherwise testing would stop there!) and the second one is broken.
What are the probabilities for each of those? Then how do you calculate p(2)?
 
  • #3
That's what I can't figure out. Is p(2) asking what the chance of the second draw being defective?
 
  • #4
It says "Y is the number of firing pins the inspector must test." and that they will stop testing the moment a defective item is found.

p(2) is "sloppy" shorthand for P(Y = 2), that is: when you do the experiment, what is the probabillity of finding the value 2 for Y.
 
  • #5
cue928 said:
That's what I can't figure out. Is p(2) asking what the chance of the second draw being defective?

After finding that the first item is non-defective, how many items are left to test? How many of those are defective?

RGV
 

Related to Expected values for random variables

What are expected values for random variables?

Expected values for random variables are a measure of the average or central tendency of a probability distribution. They represent the theoretical mean or long-term average of a random variable, taking into account both the possible outcomes and their probabilities.

How are expected values calculated?

Expected values are calculated by multiplying each possible outcome of a random variable by its corresponding probability, and then summing these products. It is represented mathematically as E(X) = ∑xP(x), where X is the random variable, x is a possible outcome, and P(x) is the probability of that outcome.

Why are expected values important in statistics?

Expected values are important in statistics because they provide a numerical summary of the data and help in making predictions about future outcomes. They also serve as a basis for other statistical measures, such as variance and standard deviation.

What is the difference between expected value and actual value?

Expected value is a theoretical measure of the average outcome of a random variable, while actual value is the observed or realized outcome. Expected value is based on probabilities, while actual value is based on data. In some cases, the expected value and actual value may be the same, but in others, there may be a difference due to chance or other factors.

Can expected values be negative?

Yes, expected values can be negative. In cases where the random variable has a mix of positive and negative outcomes, the expected value can be negative. This indicates that, on average, the outcomes tend to be less than 0. However, it is also possible for the expected value to be negative even if all the outcomes are positive, if the probabilities are such that the negative outcomes have a higher likelihood of occurring.

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