Random sampling distributions in NumPy - Mini Project: Build & Apply

Random Sampling Distributions

📖 Scenario: Imagine you are a scientist studying the heights of plants in a garden. You want to understand how the average height changes when you take different random samples from the garden.

🎯 Goal: You will create a list of plant heights, set a sample size, take many random samples from the list, calculate the average height for each sample, and finally display the list of these sample averages.

📋 What You'll Learn

Create a list called plant_heights with exact values: 10, 15, 14, 20, 18, 22, 17, 19, 16, 21

Create a variable called sample_size and set it to 4

Use a for loop to take 5 random samples of size sample_size from plant_heights using numpy.random.choice

Calculate the average height of each sample and store these averages in a list called sample_averages

Print the list sample_averages to see the results

💡 Why This Matters

🌍 Real World

Scientists and researchers often take random samples from a population to estimate average values without measuring everyone.

💼 Career

Understanding sampling distributions is important for data analysts and scientists to make predictions and decisions based on sample data.

1

Create the plant heights list

Create a list called plant_heights with these exact values: 10, 15, 14, 20, 18, 22, 17, 19, 16, 21

NumPy

# Create the list plant_heights with the given values
# Your code here

Need a hint?

Use square brackets [] to create the list and separate numbers with commas.

2

Set the sample size

Create a variable called sample_size and set it to 4

NumPy

plant_heights = [10, 15, 14, 20, 18, 22, 17, 19, 16, 21]
# Set sample_size to 4
# Your code here

Need a hint?

Just assign the number 4 to the variable sample_size.

3

Take random samples and calculate averages

Import numpy as np. Create an empty list called sample_averages. Use a for loop with variable i running 5 times. Inside the loop, take a random sample of size sample_size from plant_heights using np.random.choice with replace=True. Calculate the average of this sample using np.mean and append it to sample_averages.

NumPy

plant_heights = [10, 15, 14, 20, 18, 22, 17, 19, 16, 21]
sample_size = 4
# Import numpy as np
# Create sample_averages list
# Use a for loop to take 5 samples and append their averages
# Your code here

Need a hint?

Remember to import numpy first. Use np.random.choice to pick random samples with replacement. Use np.mean to find the average.

4

Print the sample averages

Print the list sample_averages to see the average heights of the 5 random samples.

NumPy

import numpy as np

plant_heights = [10, 15, 14, 20, 18, 22, 17, 19, 16, 21]
sample_size = 4

sample_averages = []
for i in range(5):
    sample = np.random.choice(plant_heights, size=sample_size, replace=True)
    avg = np.mean(sample)
    sample_averages.append(avg)

# Print the sample_averages list
# Your code here

Need a hint?

Use print(sample_averages) to show the list of averages.