Least squares method

PrepNuggets

A mathematical method used to determine the best fitting line for a simple linear regression model. It works by minimizing the sum of the squared differences between the observed values and the predicted values of the dependent variable.

In simple terms, the least squares method tries to find the line that best fits the data by minimizing the distance between the observed values and the predicted values. This is done by adjusting the slope and intercept of the line until the sum of the squared errors (SSE) is as small as possible.

The least squares method is commonly used in simple linear regression because it allows for an accurate prediction of the dependent variable based on the independent variable. It is a useful tool for understanding the relationship between two variables and making predictions about future outcomes.

See also: SSE