What is a Linear regression?

Curious about how data scientists predict trends or uncover relationships in data? Linear regression is one of the foundational tools they use. Let’s explore what linear regression is, how it works, and where it’s most effective.

Linear regression is a statistical technique used to model the relationship between a dependent variable (the variable you want to predict) and one or more independent variables (the variables used for prediction). It’s a straightforward and widely adopted method for analyzing data, making predictions, and drawing conclusions.

The core idea behind linear regression is the assumption of a linear relationship between the dependent and independent variables. This means that as the value of the independent variable(s) changes, the dependent variable changes in a predictable, linear fashion. For instance, if the dependent variable is a stock price and the independent variable is time, the price may rise or fall at a constant rate over time.

To perform linear regression, you’ll need a dataset containing both the dependent and independent variables. With this data, you can use a mathematical formula to fit a straight line, representing the linear relationship between the variables. This fitted line can then be used to predict the dependent variable based on new values of the independent variable(s).