Actuary R Programming

# Actuary CS1 B R Programming Solutions Part 4

Question 1(iv): Comment on the sampling distribution of the sample mean and the application of the CLT in this case based on your answers to above questions

The Exponential distribution is a continuous probability distribution that describes the time between events in a Poisson process, where events occur at a constant average rate. It is commonly used in various fields, including statistics, engineering, and science, to model phenomena where the waiting time between events follows a particular pattern.

Key characteristics of the Exponential distribution:

1. Probability Density Function (PDF): The probability density function of the Exponential distribution is given by:
[f(x; \lambda) = \begin{cases}
\lambda e^{-\lambda x} & \text{for } x \geq 0 \
0 & \text{for } x < 0
\end{cases}]

Where:

• (x) is the random variable (usually representing time or waiting time).
• (\lambda) (lambda) is the rate parameter. It is a positive constant that determines the average rate at which events occur. It is also equal to the reciprocal of the mean ((\lambda = 1/\text{mean})).
1. Mean and Variance:
• The mean of the Exponential distribution is (1/\lambda).
• The variance is (1/\lambda^2).
1. Memoryless Property: The Exponential distribution has the memoryless property, which means that the probability of an event occurring in the future does not depend on how much time has already passed.
2. Example Applications:
• Modeling time between arrivals of customers at a service center.
• Modeling the lifetime of electronic components.
• Analyzing the duration of phone calls in a call center.

The rate parameter ((\lambda)) is a crucial parameter of the Exponential distribution. It controls the rate at which events occur. A higher value of (\lambda) corresponds to a shorter average waiting time between events, while a lower value of (\lambda) corresponds to a longer average waiting time.

Based on Previous Answers, we can make the following comments on the sampling distribution of the sample mean and the application of the Central Limit Theorem (CLT) in this case:

1. Sampling Distribution of the Sample Mean: The histogram of the sample means generated from 10,000 samples of size 10 from an Exponential distribution with a rate parameter of 3 demonstrates that the distribution of sample means tends to be approximately normally distributed. This is evident from the shape of the histogram, which resembles a bell curve, and is a key characteristic of the CLT.
2. Application of the Central Limit Theorem (CLT): The CLT states that the sampling distribution of the sample mean, regardless of the underlying population distribution, approaches a normal distribution as the sample size increases. In this case:
• Population Distribution: The underlying population distribution is Exponential with a rate parameter of 3.
• Sample Size: Each sample consists of 10 observations.
The CLT was applied to calculate the theoretical mean and standard deviation of the sampling distribution of the sample mean. These theoretical values were used to plot a normal distribution curve, which was superimposed on the histogram of the sample means.
• Theoretical Mean: The theoretical mean of the sampling distribution of the sample mean, as predicted by the CLT, is the same as the population mean, which is 1/3 (approximately 0.3333).
• Theoretical Standard Deviation: The theoretical standard deviation of the sampling distribution of the sample mean, as predicted by the CLT, is the population standard deviation (1/3) divided by the square root of the sample size (√10), resulting in approximately 0.1054.
The superimposed normal distribution curve closely aligns with the histogram, confirming the applicability of the CLT in this case. This agreement between the empirical sampling distribution and the theoretical normal distribution is a hallmark of the CLT and illustrates how the sample mean tends to follow a normal distribution as the sample size increases, regardless of the original population distribution.

In summary, the sampling distribution of the sample mean in this case exhibits characteristics consistent with the CLT. This demonstrates the power of the CLT to describe how the distribution of sample means approaches normality as sample size increases, which is a fundamental concept in statistics and allows for making inferences about population parameters based on sample data.

## Conclusion

The Exponential distribution is used to model the time between events in a Poisson process, and the rate parameter ((\lambda)) determines the average rate at which events occur, with a higher value of (\lambda) leading to shorter waiting times between events.

Check out the answer to previous question here

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