Statistics Gamma Distribution question

In summary, to solve for P(X ≥ 2αβ) in the given problem, you can use the property of the gamma function to simplify the expression in the integral. For the second part of the question, eαtM(t) can be solved for using the given theorem, by substituting the appropriate values for α and β. If you have any further questions, please don't hesitate to ask. Good luck with your problem!
  • #1
stevenham
8
0

Homework Statement


Let X have a gamma distribution with parameters α and β.
Show that P(X ≥ 2αβ) ≤ (2/e)2

Homework Equations



f(x) = pfd of a Gamma

The Attempt at a Solution



I began by solving for P(X ≥ 2αβ) by doing ∫ f(x) dx from 2αβ to ∞
I set y=x/β for substitution.
and I got up to 1/[itex]\Gammaα[/itex] * ∫yα-1 e-y dy from 2αβ to ∞

I don't really know what to do from here.
I know that ∫yα-1 e-y dy from 0 to ∞ = [itex]\Gammaα[/itex]
but I'm kind of lost because of the 2αβ

I am not even sure if this approach to solving this problem is correct.

For the second part of the question, we have a theorem that says :
P(X≥ α) ≤ eαtM(t)

My guess for the second part is to simply solve for eαtM(t)
Is that correct?

Any help will be greatly appreciated. Thank you.
 
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  • #2



Thank you for your post. I am a scientist and I would be happy to help you with this problem. Your approach to solving this problem is correct so far. To continue, you can use the property of the gamma function which states that Γ(x+1) = xΓ(x) for any positive real number x. This will allow you to simplify the expression in the integral and solve for P(X ≥ 2αβ).

For the second part of the question, your approach is also correct. You can use the theorem you mentioned to solve for eαtM(t). Remember to substitute the appropriate values for α and β in the expression.

I hope this helps. If you have any further questions, please don't hesitate to ask. Good luck with your problem!
 

Related to Statistics Gamma Distribution question

1. What is a Gamma distribution in statistics?

A Gamma distribution is a probability distribution that is commonly used to model continuous variables with positive values, such as waiting times, income, and sizes of objects. It is characterized by two parameters: shape (α) and scale (β) that determine the shape and spread of the distribution.

2. How is a Gamma distribution different from other distributions?

A Gamma distribution differs from other distributions in that it is strictly positive, meaning it has no values less than or equal to zero. It also has a skewed shape, with a longer tail on the right side, making it useful for modeling data with a long tail or outliers.

3. What are the applications of Gamma distribution in real life?

Gamma distribution has various applications in real life, such as modeling insurance claims, rainfall data, lengths of telephone calls, and radioactive decay. It is also commonly used in survival analysis, where the time until an event occurs is modeled, such as time to failure or time to death.

4. How do you calculate probabilities using a Gamma distribution?

To calculate probabilities using a Gamma distribution, you need to know the values of the shape and scale parameters (α and β). Then, you can use a statistical software or a Gamma distribution table to find the corresponding probability for a given value. Alternatively, you can use the Gamma distribution formula to calculate the probability manually.

5. Can a Gamma distribution have a negative shape parameter?

No, a Gamma distribution's shape parameter (α) must be a positive value, as it defines the shape of the distribution. A negative value for α would result in an impossible distribution. However, the scale parameter (β) can be negative, which would result in a reflected version of the distribution.

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