Nonlinear least squares
A)solves the minimization of the sum of squared predictive mistakes through sophisticated mathematical routines, essentially by trial and error methods.
B)should always be used when you have nonlinear equations.
C)gives you the same results as maximum likelihood estimation.
D)is another name for sophisticated least squares.

Question

Quizplus · Accepted Answer

Nonlinear least squares is a method to minimize the sum of squared predictive mistakes, but it does not necessarily use trial and error methods. It is an iterative optimization algorithm that uses gradient descent or other optimization routines to arrive at the solution. Thus, option B is incorrect. Nonlinear least squares is related to maximum likelihood estimation in that they both aim to find parameters that maximize the goodness of fit of the model to the data. However, they are not the same, as nonlinear least squares is a specific method for solving the optimization problem, while maximum likelihood is a general framework for statistical inference. Therefore, option C is incorrect. Finally, while nonlinear least squares is a sophisticated method, it is not the same as sophisticated least squares, as the latter term is more general and may refer to any advanced form of least squares regression. Thus, option D is incorrect.

Nonlinear Least Squares