Statistics: Expectations Question

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In summary, for the first problem, if an antique coin collection worth $20,000 is insured by an insurance company for an annual premium of $300 and the probability of it being stolen is 0.002, the expected profit for the company is $260.For the second problem, if a person wins $5 every time they roll "doubles" with two dice, the game is fair if they pay $0.83 to play, assuming "doubles" means getting the same number on both dice.
  • #1
shawnz1102
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Hi guys, I'm having a hard time figuring out these two expectation problems below. Can someone please help me?

An insurance company insures a person's antique coin collection worth $20,000 for an annual premium of $300. If the company figures that the probability of the collection being stolen is 0.002, what will be the company's expected profit?

For this problem, here's what I got:
x...300...?
p(x)...0.998...0.002

I can't seem to figure out what to put in the ? (loss). Maybe i just don't understand the wording of the problem... I mean, it's saying that if the antique does get lost, the company would pay the insurer $20,000? If not, then there's really no way of figuring out what the company's loss is since it doesn't say it...

The answer is: $260.

If a person rolls doubles when he tosses two dice, he wins $5. For the game to be fair, how much should the person pay to play the game?

For this problem, I have:

x....5 ...?
p(x)...1/12...11/12

The reason I got 1/12 for the winning side is because there's 3 chances of getting doubles (rolling a 4 & 6, 5 & 6, and 6 & 6), and the total possible outcome is 36 (6 x 6)

And for the 11/12, I just subtracted 12/12 from 1/12. And this is as far as I got...

The answer is: $0.83Any help would be appreciated. Thanks in advance!
 
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  • #2
Clearly, is there is a 0.002 chance of it being stolen - then that is the probability they have to pay out $20,000. So the expected loss is (20,000)*(0.002) = $40. Take that away from the $300 they would make otherwise... and the expected profit is $260, as desired.

As for the second problem - what is "doubles"? I was assuming that "doubles" means double 1s, double 2s, etc. In which case there are six possibilities of getting this, which means he stands 1/6 of a chance of doing so. Try this hint instead.
 
  • #3
Thanks, got the right answer!
 

FAQ: Statistics: Expectations Question

What is the concept of "expectation" in statistics?

The concept of "expectation" in statistics refers to the average value or outcome that is expected to occur in a given situation. It is calculated by taking into account all possible outcomes and their respective probabilities.

How is expectation calculated in statistics?

The calculation of expectation involves multiplying each possible outcome by its respective probability and then summing up these values. This can be represented mathematically as E(X) = x1p1 + x2p2 + ... + xnpn, where X represents the random variable and x1 to xn are the possible outcomes with their respective probabilities p1 to pn.

What is the difference between expected value and expected outcome?

Expected value and expected outcome are two terms that are often used interchangeably but have different meanings. Expected value refers to the overall average outcome that is expected to occur, while expected outcome refers to the specific outcome that is expected to occur in a particular situation.

Why is expectation an important concept in statistics?

Expectation is an important concept in statistics as it allows us to make predictions and draw conclusions about a population based on a sample. It also helps in decision-making and understanding the likelihood of certain outcomes in a given situation.

What are some real-life examples where expectation is used in statistics?

Expectation is used in various real-life situations, such as predicting stock market trends, determining insurance premiums, and analyzing survey data. It is also used in games of chance, such as rolling a dice or flipping a coin, to calculate the expected outcome and make informed decisions.

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