Minimizing the sum of the squared deviations around the line is called Least square estimation.
It is given that the sum of squares is around the line.
Least squares estimations minimize the sum of squared deviations around the estimated regression function. It is between observed data, on the one hand, and their expected values on the other. This is called least squares estimation because it gives the least value for the sum of squared errors. Finding the best estimates of the coefficients is often called “fitting” the model to the data, or sometimes “learning” or “training” the model.
Carmel would need 7 gas tanks. (2,240 ÷ 320 = 7) Carmel would need $273 because since she needs $39 dollars for one gas tank, you would have to multiply that by 7. (39 x 7 = $273)
