How Do Mean and Variance Relate in a Gamma Distribution?

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  • #1
mcguiry03
8
0
prove that μ=αθ and σ^2=αθ^2




μ = E(X)
...= ∫(x = 0 to ∞) x *(1/(θ^α Γ(α))) x^(α-1) e^(-x/θ) dx
...= (1/(θ^α Γ(α))) ∫(x = 0 to ∞) x^α e^(-x/θ) dx
...= (1/(θ^α Γ(α))) ∫(t = 0 to ∞) (tθ)^α e^(-t) * (θ dt), letting t = x/θ
...= (θ/Γ(α)) ∫(t = 0 to ∞) t^α e^(-t) dt
...= (θ/Γ(α)) Γ(α+1), by definition of Gamma function
...= (θ/Γ(α)) * α Γ(α), by Gamma recurrence
...= αθ.

E(X^2) = ∫(x = 0 to ∞) x^2 * (1/(θ^α Γ(α))) x^(α-1) e^(-x/θ) dx
...= (1/(θ^α Γ(α))) ∫(x = 0 to ∞) x^(α+1) e^(-x/θ) dx
...= (1/(θ^α Γ(α))) ∫(t = 0 to ∞) (tθ)^(α+1) e^(-t)* (θ dt), letting t = x/θ
...= (θ^2/Γ(α)) ∫(t = 0 to ∞) t^(α+1) e^(-t) dt
...= (θ^2/Γ(α)) Γ(α+2), by definition of Gamma function
...= (θ^2/Γ(α)) * α(α+1) Γ(α), by Gamma recurrence (used twice)
...= α(α+1)θ^2.

So, σ^2 = E(X^2) - (E(X))^2 = α(α+1)θ^2 - (αθ)^2 = αθ^2




i would like to know if it is correct and tnx :biggrin:
 
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  • #2
mcguiry03 said:
prove that μ=αθ and σ^2=αθ^2




μ = E(X)
...= ∫(x = 0 to ∞) x *(1/(θ^α Γ(α))) x^(α-1) e^(-x/θ) dx
...= (1/(θ^α Γ(α))) ∫(x = 0 to ∞) x^α e^(-x/θ) dx
...= (1/(θ^α Γ(α))) ∫(t = 0 to ∞) (tθ)^α e^(-t) * (θ dt), letting t = x/θ
...= (θ/Γ(α)) ∫(t = 0 to ∞) t^α e^(-t) dt
...= (θ/Γ(α)) Γ(α+1), by definition of Gamma function
...= (θ/Γ(α)) * α Γ(α), by Gamma recurrence
...= αθ.

E(X^2) = ∫(x = 0 to ∞) x^2 * (1/(θ^α Γ(α))) x^(α-1) e^(-x/θ) dx
...= (1/(θ^α Γ(α))) ∫(x = 0 to ∞) x^(α+1) e^(-x/θ) dx
...= (1/(θ^α Γ(α))) ∫(t = 0 to ∞) (tθ)^(α+1) e^(-t)* (θ dt), letting t = x/θ
...= (θ^2/Γ(α)) ∫(t = 0 to ∞) t^(α+1) e^(-t) dt
...= (θ^2/Γ(α)) Γ(α+2), by definition of Gamma function
...= (θ^2/Γ(α)) * α(α+1) Γ(α), by Gamma recurrence (used twice)
...= α(α+1)θ^2.

So, σ^2 = E(X^2) - (E(X))^2 = α(α+1)θ^2 - (αθ)^2 = αθ^2




i would like to know if it is correct and tnx :biggrin:

It looks OK.

RGV
 

Related to How Do Mean and Variance Relate in a Gamma Distribution?

1. What is the gamma distribution and why is it important in statistics?

The gamma distribution is a continuous probability distribution that is frequently used to model the time to failure of certain types of systems. It is also used to model the waiting time between events in a Poisson process. The gamma distribution is important in statistics because it allows us to model data that is positively skewed and has a non-negative range.

2. How is the gamma distribution different from other common distributions like the normal or exponential distribution?

The main difference between the gamma distribution and other common distributions is that it has two parameters, shape and scale, instead of just one. This allows the gamma distribution to have a more flexible shape than distributions with only one parameter. Additionally, the gamma distribution is typically used to model data that is positively skewed, while the normal distribution is used for symmetric data and the exponential distribution is used for data with a constant failure rate.

3. How can you prove that a dataset follows a gamma distribution?

To prove that a dataset follows a gamma distribution, you can use a statistical test like the Kolmogorov-Smirnov test or the chi-square goodness of fit test. These tests compare the observed data to the expected values from a gamma distribution and determine if there is a significant difference between the two. If the p-value is greater than the chosen significance level, then we can conclude that the data follows a gamma distribution.

4. Can the gamma distribution be used for both continuous and discrete data?

The gamma distribution is typically used for continuous data, but it can also be used for discrete data if the data is binned into intervals. However, this may not accurately represent the true distribution of the data and it is generally recommended to use a distribution specifically designed for discrete data, such as the Poisson or binomial distribution.

5. How is the gamma distribution related to other distributions like the chi-square and beta distributions?

The chi-square distribution is a special case of the gamma distribution, where the shape parameter is equal to the degrees of freedom. The beta distribution is also related to the gamma distribution, as it is a ratio of two gamma distributions. The beta distribution is commonly used to model data that is bounded between 0 and 1, while the gamma distribution has a broader range and can be used for data that is not bounded.

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