Introduction
Linear regression is one of the simplest and most commonly used methods for predicting outcomes or understanding relationships between two variables. For example:
- In a business setting, you might use it to predict sales based on advertising spending.
- In health care, you could predict a patient’s recovery time based on the dosage of a drug.
- In sports, you could predict a player’s performance based on their training hours.
The reason linear regression is so popular is that it’s straightforward to calculate, easy to interpret, and works well when the assumptions listed above are met. However, if those assumptions are violated (for example, if the relationship isn’t linear or the errors are very irregular), the results can be misleading.