Actuary R Programming

# Simulating Samples to Investigate Electric Car Proportions in Two Populations

## Introduction:

In this blog post, we’ll explore how to simulate samples to investigate the proportions of electric cars in two large populations, denoted as populations A and B. This simulation will help us estimate the proportions of electric cars in these populations. We’ll use the R programming language and ensure reproducibility by setting a random seed.

## Step 1: Setting the Stage

Before we dive into the simulation, we need to establish some key parameters:

• Proportion of electric cars in population A: 0.02
• Proportion of electric cars in population B: 0.025
• Sample size for population A: 900
• Sample size for population B: 1,200

## Step 2: Simulating the Samples

To simulate the samples, we’ll use R’s built-in random number generation capabilities. Specifically, we’ll use the `sample` function, which allows us to randomly select from a set of options.

Here’s the code snippet to simulate the samples:

```# Set the seed for reproducibility
set.seed(12345)

# Define the parameters
proportion_A <- 0.02  # Proportion of electric cars in population A
proportion_B <- 0.025  # Proportion of electric cars in population B
nA <- 900  # Sample size for population A
nB <- 1200  # Sample size for population B

# Simulate samples for populations A and B
sample_A <- sample(c("Electric", "Non-electric"), size = nA, replace = TRUE, prob = c(proportion_A, 1 - proportion_A))
sample_B <- sample(c("Electric", "Non-electric"), size = nB, replace = TRUE, prob = c(proportion_B, 1 - proportion_B))

# Save the simulated samples for later use
save(sample_A, sample_B, file = "simulated_samples.RData")
```

In this code, we first set the random seed to ensure that our results are reproducible. We then define the parameters for our simulation, including the proportions and sample sizes. Next, we use the `sample` function to create random samples for both populations, considering the specified proportions. Finally, we save the simulated samples for future analysis.

## Conclusion:

Simulating samples is a fundamental step in many data analysis and research projects. In this blog post, we’ve demonstrated how to simulate samples to investigate the proportions of electric cars in two populations. With the simulated data in hand, we can now proceed to estimate the proportions and conduct further analyses to gain insights into these populations. This approach allows us to make informed decisions and draw meaningful conclusions based on our research.

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