What is a statistical method used to fit a linear model to a data set?

Linear regression is a statistical method specifically designed to investigate the relationship between independent variables and a dependent variable by fitting a linear equation to observed data. This technique aims to model the dependency of the dependent variable on one or more independent variables. The resulting linear equation can then be used to predict the value of the dependent variable based on given values of the independent variables.

In linear regression, the best-fitting line is determined through a process that minimizes the sum of the squares of the vertical distances of the points from the line, known as the least squares method. This approach is widely used due to its simplicity and effectiveness in capturing the overall trend in the data.

The focus on modeling relationships mathematically makes linear regression distinct from the other options presented. Causal analysis often involves more complex relationships and testing of hypotheses about cause-and-effect rather than merely fitting a straight line to data. Statistical inference encompasses a broader category that deals with drawing conclusions about populations based on sample data, but does not refer specifically to the process of fitting a linear model. Data mining involves extracting patterns from large data sets and can utilize a variety of statistical methods, including linear regression, but is not primarily focused on fitting a linear model itself.