What does linear regression analyze in statistics?

Linear regression is a statistical method used to assess the relationship between numeric variables. It aims to model the relationship between a dependent variable and one or more independent variables by fitting a linear equation to observed data. In this context, the dependent variable is the outcome being predicted or explained, while the independent variables are the predictors or factors believed to influence that outcome.

The process involves identifying how changes in the independent variable(s) affect the dependent variable. For example, if researchers are trying to understand how study hours (independent variable) influence test scores (dependent variable), linear regression allows them to quantify and express this relationship in a predictive model.

This technique assumes a linear correlation, meaning that it looks for a straight-line relationship between the variables. Thus, linear regression is foundational for understanding and quantifying relationships in datasets where both the dependent and independent variables are numeric, helping to make predictions and informed decisions based on that analysis.