Using Chebyshev's Theorem (and another minor question)

In summary, using Chebyshev's Theorem, we can find an upper bound for P(X = 21) by solving the inequality -k < 3 < k and taking a value of k > 3. For the second question, we can use the fact that P(A U B) = P(A) + P(B) - P(A intersection B) to find a range for P(A intersection B). Since P(A) + P(B) > 1, there must be some overlap between A and B, therefore the smallest possible value for P(A intersection B) is 1/12 and the largest possible value is 1/3.
  • #1
Hiche
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Homework Statement



If X is a discrete random variable with mean u = 12 and variance = 9, use Chebyshev's Theorem to find an upper bound for P(X = 21).

Homework Equations





The Attempt at a Solution



Now, I'm not sure about this since there are different upper bounds, right?

P(|21 - 3k < 21 < 21 + 3k|) ≤ 1 / k^2. We solve the inner inequality and we get -k < 3 < k. To find an upper bound, do we simply take a value k > 3?

And an unrelated question (just not to make another thread):

Let A and B be two events with P(A) = 3/4 and P(B) = 1/3. Explain why 1/12 ≤ P(A intersection B) ≤ 1/3. How do we approach this exactly?
 
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  • #2
regarding your second question, just something to think about: what is P(A Union B)? what does this tell you about P(A intersect B)?
 
  • #3
P(A U B) = P(A) + P(B) - P(A intersection B) = 3/4 + 1/3 - P(A intersection B) = 13/12 - P(A intersection B). So, when P(A intersection B) = 1/12, P(A U B) = 1 and when it's 1/3, P(A U B) = 3/4. Does it have to do something with that? It looks too obvious but I'm not figuring it out!
 
  • #4
the fact that P(A) + P(B) > 1 tells you there has to be some overlap, right? So you're looking at kind of a best case/worst case scenario. How small could the overlap be? How large could the overlap be?
 

1. What is Chebyshev's Theorem?

Chebyshev's Theorem, also known as the Chebyshev inequality, is a mathematical theorem that provides a bound on the probability that a random variable will deviate from its mean by a certain amount. It applies to any probability distribution, making it a useful tool in statistics and probability theory.

2. How is Chebyshev's Theorem used?

Chebyshev's Theorem is used to estimate the likelihood of a random variable falling within a certain range of values. It allows us to make statements about the probability of an event occurring, even when we don't know the exact shape of the probability distribution.

3. What is the formula for Chebyshev's Theorem?

The formula for Chebyshev's Theorem is P(|X-μ| ≥ kσ) ≤ 1/k2, where X is the random variable, μ is the mean, σ is the standard deviation, and k is the number of standard deviations from the mean.

4. Can Chebyshev's Theorem be applied to any probability distribution?

Yes, Chebyshev's Theorem can be applied to any probability distribution, as long as it has a finite mean and standard deviation. This includes both discrete and continuous distributions.

5. How accurate is Chebyshev's Theorem?

Chebyshev's Theorem provides an upper bound on the probability of an event occurring, so it may not give an exact probability. However, as the number of standard deviations from the mean increases, the accuracy of the theorem also increases. For example, if k=2, the theorem guarantees that the probability is at most 25%. If k=3, the probability is at most 11.1%, and so on.

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