Final answer:
Least squares used with two or more independent variables is called multiple regression, aiming to minimize the SSE to find a best-fit plane or hyperplane.
Step-by-step explanation:
When least squares is used to fit an equation with two or more independent variables, it's known as multiple regression or multivariable linear regression. This statistical technique extends the concept of a best-fit line in simple linear regression (where there's just one independent variable) to higher dimensions, creating a best-fit plane or hyperplane. Just like in simple linear regression, the goal is to minimize the sum of squared errors (SSE), which represents the sum of the squares of the differences between the observed and predicted values.
In the context of the provided information, linear regression involves using a line of best fit to make predictions based on the data collected. The least-squares regression line is obtained by determining the values of the intercept and slope that minimize the SSE, using the formula y = a + bx, where 'a' is the y-intercept, 'b' is the slope, and 'x' is the independent variable. It's important to note that while regression can be used for prediction within the set of data, it may not be accurate for extrapolation outside the dataset.