Linear regression basics in Data Analysis Python - Mini Project: Build & Apply

Linear regression basics

📖 Scenario: You are a data analyst at a small company. You want to understand how advertising spending affects sales. You have data for advertising spending and sales for 5 months.

🎯 Goal: Build a simple linear regression model to find the relationship between advertising spending and sales.

📋 What You'll Learn

Create a dictionary with advertising spending and sales data

Create a variable for the number of data points

Calculate the slope and intercept of the best fit line using the linear regression formula

Print the slope and intercept values

💡 Why This Matters

🌍 Real World

Linear regression is used in business to predict sales based on advertising budgets, helping companies plan their spending.

💼 Career

Data analysts and data scientists use linear regression to find relationships between variables and make predictions.

1

Create the data dictionary

Create a dictionary called data with two keys: 'advertising' and 'sales'. The value for 'advertising' should be the list [5, 10, 15, 20, 25]. The value for 'sales' should be the list [7, 12, 17, 22, 27].

Data Analysis Python

# Create the data dictionary with advertising and sales lists
# Your code here

Hint

Use a dictionary with keys 'advertising' and 'sales'. Each key should have a list of numbers as its value.

2

Set the number of data points

Create a variable called n and set it to the length of the data['advertising'] list.

Data Analysis Python

data = {'advertising': [5, 10, 15, 20, 25], 'sales': [7, 12, 17, 22, 27]}
# Create variable n for number of data points
# Your code here

Hint

Use the len() function on data['advertising'] to get the number of points.

3

Calculate slope and intercept

Calculate the slope m and intercept b of the best fit line using the formulas:
m = (n * sum(x*y) - sum(x) * sum(y)) / (n * sum(x*x) - (sum(x))**2)
b = (sum(y) - m * sum(x)) / n
Use x for data['advertising'] and y for data['sales']. Use sum() and list comprehensions to calculate sums.

Data Analysis Python

data = {'advertising': [5, 10, 15, 20, 25], 'sales': [7, 12, 17, 22, 27]}
n = len(data['advertising'])
# Calculate slope m and intercept b using linear regression formulas
# Your code here

Hint

Use zip(x, y) to multiply pairs of x and y values. Calculate sums first, then apply the formulas.

4

Print the slope and intercept

Print the slope m and intercept b values using two separate print() statements. Print "Slope:" followed by m, and "Intercept:" followed by b.

Data Analysis Python

data = {'advertising': [5, 10, 15, 20, 25], 'sales': [7, 12, 17, 22, 27]}
n = len(data['advertising'])
x = data['advertising']
y = data['sales']
sum_x = sum(x)
sum_y = sum(y)
sum_xy = sum(x_i * y_i for x_i, y_i in zip(x, y))
sum_xx = sum(x_i * x_i for x_i in x)
m = (n * sum_xy - sum_x * sum_y) / (n * sum_xx - sum_x ** 2)
b = (sum_y - m * sum_x) / n
# Print slope m and intercept b
# Your code here

Hint

Use print("Slope:", m) and print("Intercept:", b) to show the results.