Covariance with np.cov() in NumPy - Mini Project: Build & Apply

Calculate Covariance Using np.cov()

📖 Scenario: You are analyzing the relationship between hours studied and exam scores for a group of students. Understanding how these two variables move together can help predict performance.

🎯 Goal: Build a small program that calculates the covariance matrix of hours studied and exam scores using np.cov().

📋 What You'll Learn

Create two lists named hours_studied and exam_scores with exact values.

Create a variable called bias_flag to configure the covariance calculation.

Use np.cov() with bias=bias_flag to calculate the covariance matrix.

Print the covariance matrix.

💡 Why This Matters

🌍 Real World

Covariance helps understand how two variables change together, useful in finance, health, and education to find relationships.

💼 Career

Data scientists use covariance matrices to analyze data relationships and prepare for more advanced techniques like principal component analysis.

1

Create the data lists

Create a list called hours_studied with values [2, 4, 6, 8, 10] and a list called exam_scores with values [50, 60, 70, 80, 90].

NumPy

# Create the lists hours_studied and exam_scores with the exact values
# Your code here

Need a hint?

Use square brackets to create lists with the exact numbers given.

2

Set the bias flag for covariance

Create a variable called bias_flag and set it to False to calculate the unbiased covariance matrix.

NumPy

hours_studied = [2, 4, 6, 8, 10]
exam_scores = [50, 60, 70, 80, 90]
# Create bias_flag and set it to False
# Your code here

Need a hint?

Set bias_flag exactly to False.

3

Calculate the covariance matrix

Import numpy as np and create a variable called cov_matrix that stores the covariance matrix of hours_studied and exam_scores using np.cov() with bias=bias_flag. Use a list of the two lists as input.

NumPy

hours_studied = [2, 4, 6, 8, 10]
exam_scores = [50, 60, 70, 80, 90]
bias_flag = False
# Import numpy as np and calculate cov_matrix using np.cov with bias=bias_flag
# Your code here

Need a hint?

Use np.cov() with a list containing hours_studied and exam_scores and set bias=bias_flag.

4

Print the covariance matrix

Print the variable cov_matrix to display the covariance matrix.

NumPy

import numpy as np

hours_studied = [2, 4, 6, 8, 10]
exam_scores = [50, 60, 70, 80, 90]
bias_flag = False

cov_matrix = np.cov([hours_studied, exam_scores], bias=bias_flag)
# Print the covariance matrix
# Your code here

Need a hint?

Use print(cov_matrix) to show the covariance matrix.