Calculating a Least Squares Regression Line: Equation, Example, Explanation If you want a simple explanation of how to calculate and draw a line of best fit through your data, read on!

The least-squares criterion is a method of measuring the accuracy of a line in depicting the data that was used to generate it. That is, the formula determines the line of best fit.

The least-squares method of cost estimation involves using mathematical regression techniques to calculate the slope and intercept of the best-fit line for the costs used in estimation.

Consider a heteroscedastic linear regression model in which responses are grouped so that the variance is constant within each group. Let β denote the vector of regression parameters and let θ denote ...

Classical least squares regression consists of minimizing the sum of the squared residuals. Many authors have produced more robust versions of this estimator by replacing the square by something else, ...

The residual sum of squares (RSS) is a statistical technique used to measure the variance in a data set that is not explained by the regression model.

Defines the least-squares means for the fixed-effects general linear model. The report also discusses the use of least-squares means in lieu of class or subclass arithmetic means with unbalanced ...