 # Gamma Distribution Explained: Applications in Finance, Engineering, and More

## Introduction

Probability distributions are essential tools in data analysis and modeling. Gamma distribution is a popular continuous probability distribution that can be used to model various real-world phenomena such as waiting times, incomes, and insurance claims. In this article, we will explore the intuition, derivation, and examples of the gamma distribution.

## What is Gamma Distribution?

Gamma distribution is a continuous probability distribution that has two parameters, shape (α) and scale (β). It is often denoted as Gamma(α,β). The probability density function (PDF) of gamma distribution is given as:

​where x ≥ 0, α > 0, β > 0, and Γ(α) is the gamma function.

The gamma distribution has a variety of shapes depending on the values of its parameters. When α = 1, the gamma distribution reduces to the exponential distribution. When α is an integer, the gamma distribution reduces to the Erlang distribution.

## Derivation of Gamma Distribution

The gamma distribution can be derived in various ways. One way is to use the moment generating function (MGF) of a random variable X. The MGF of X is defined as:

Using the MGF, we can derive the probability density function (PDF) of X. For the gamma distribution, the MGF is given as:

Taking the derivative of the MGF and simplifying, we obtain the PDF of the gamma distribution as shown earlier.

## Properties of Gamma Distribution

Gamma distribution has several important properties. Some of these properties include:

• Mean: E[X] = αβ
• Variance: Var(X) = αβ^2
• Mode: (α-1)β
• Skewness: Skewness(X) = 2/√α
• Kurtosis: Kurtosis(X) = 6/α + 3

## Types of Gamma Distribution

There are two common types of gamma distribution, namely Erlang distribution and Chi-square distribution.

### Erlang Distribution

The Erlang distribution is a special case of the gamma distribution, where the shape parameter (α) is an integer. The Erlang distribution is often used to model the waiting times in queuing systems.

### Chi-Square Distribution

The Chi-square distribution is also a special case of the gamma distribution, where the shape parameter (α) is an integer and the scale parameter (β) is 2. The Chi-square distribution is often used in hypothesis testing and goodness-of-fit tests.

## Applications of Gamma Distribution

Gamma distribution has several applications in various fields such as finance, engineering, physics, and social sciences. Some of the common applications of gamma distribution are:

• Waiting Time: Gamma distribution can be used to model the waiting times in queuing systems such as waiting times in a hospital emergency department or waiting times at a call center.
• Income Distribution: Gamma distribution can be used to model the income distribution of a population. It has been found that the income distribution of most countries follows a gamma distribution.
• Insurance Claims: Gamma distribution can be used to model the frequency and severity of insurance claims. It can help insurance companies to estimate the risk and set premiums.

## Examples of Gamma Distribution

Let’s look at some examples to understand the practical applications of gamma distribution.

### Waiting Time

Suppose we want to model the waiting time of customers at a restaurant. We can assume that the waiting time follows a gamma distribution with shape parameter α = 3 and scale parameter β = 5. The probability density function of the waiting time is given as:

where x ≥ 0.

### Income Distribution

Suppose we want to model the income distribution of a country. We can assume that the income follows a gamma distribution with shape parameter α = 2 and scale parameter β = 10000. The probability density function of the income is given as:

where x ≥ 0.

### Insurance Claims

Suppose we want to model the number of insurance claims in a year. We can assume that the number of claims follows a gamma distribution with shape parameter α = 5 and scale parameter β = 1000. The probability mass function of the number of claims is given as:

where k is a non-negative integer.

• Flexibility: Gamma distribution is a flexible distribution that can take on a variety of shapes depending on its parameters.
• Goodness of Fit: Gamma distribution can often provide a good fit to real-world data.
• Computational Ease: Gamma distribution has a simple and well-known probability density function that can be easily computed.

Some of the disadvantages of gamma distribution include:

• Limited Domain: Gamma distribution is only defined for non-negative values of X.
• Limited Skewness: Gamma distribution has limited skewness, which can be a disadvantage in certain applications.
• Complex Parameter Estimation: Estimating the parameters of gamma distribution can be complex and time-consuming in certain cases.

## Conclusion

In this article, we explored the intuition, derivation, and examples of the gamma distribution. Gamma distribution is a popular continuous probability distribution that has a variety of applications in various fields such as finance, engineering, physics, and social sciences. It is a flexible distribution that can take on a variety of shapes depending on its parameters. It can often provide a good fit to real-world data and has a simple and well-known probability density function that can be easily computed.