Simple Linear Regression

What is Linear Regression?

Linear regression is a statistical method used to model the relationship between a dependent variable (Y) and an independent variable (X) by fitting a straight line to the observed data.
This line is known as the regression line, and its equation is:

Y=β0+β1X

Where:

• β₀ is the intercept (the value of Y when X = 0)
• β₁ is the slope (how much Y changes for a one-unit change in X)

The goal of linear regression is to find the best-fitting line that minimizes the sum of squared differences between observed and predicted values.

When to Use Linear Regression

Linear regression is used when you want to:

• Predict a numerical outcome based on another variable.
• Understand relationships between two continuous variables.
• Estimate trends or make forecasts based on existing data.

Examples of when to use it:

• Predicting a person’s salary based on years of experience.
• Estimating house prices based on square footage.
• Forecasting sales based on advertising spend.