Ordinary and Weighted Least Squares
The best-fit curve is often assumed to be that which minimizes the sum of squared residuals. This is the (ordinary) least squares (OLS) approach. However, in cases where the dependent variable does not have constant variance a sum of weighted squared residuals may be minimized; see weighted least squares. Each weight should ideally be equal to the reciprocal of the variance of the observation, but weights may be recomputed on each iteration, in an iteratively weighted least squares algorithm.
Read more about this topic: Nonlinear Regression
Famous quotes containing the words ordinary and/or squares:
“When the shrivelled skin of the ordinary is stuffed out with meaning, it satisfies the senses amazingly.”
—Virginia Woolf (1882–1941)
“An afternoon of nurses and rumours;
The provinces of his body revolted,
The squares of his mind were empty,
Silence invaded the suburbs,”
—W.H. (Wystan Hugh)