Final answer:
The method called when least squares is used to fit an equation with two or more independent variables is known as multiple regression, which involves minimizing the sum of squared differences between observed and predicted data.
Step-by-step explanation:
When least squares is used to fit an equation involving two or more independent variables, the method is called multiple regression. This statistical technique is used to model the relationship between a dependent variable and two or more independent variables by fitting a linear equation to observed data. The least squares method is used to find the line that minimizes the sum of the squared differences between the observed values and the values predicted by the model.
For example, if you wish to predict a student's grade based on the number of hours spent studying and the number of hours slept the night before the exam, you would use multiple regression. You would assign one independent variable (x1) to the hours studied and another independent variable (x2) to the hours slept.