Here are the main differences between multiple regression and linear regression:

1. Number of Predictors:

   • Linear Regression: This uses just one variable to predict another.
   • Multiple Regression: This uses two or more variables to make predictions.

2. Complexity:

   • Linear Regression: It’s simpler and easier to understand because it shows a clear relationship between the two variables.
   • Multiple Regression: This is more complex because it looks at how different variables work together.

3. Applications:

   • Linear Regression: It's great for easy and clear relationships.
   • Multiple Regression: It’s better for real-life situations where many factors affect the result.

Choosing between them depends on your data and how many variables you want to use!