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:
“Happiness is a matter of one’s most ordinary everyday mode of consciousness being busy and lively and unconcerned with self. To be damned is for one’s ordinary everyday mode of consciousness to be unremitting agonising preoccupation with self.”
—Iris Murdoch (b. 1919)
“And New York is the most beautiful city in the world? It is not far from it. No urban night is like the night there.... Squares after squares of flame, set up and cut into the aether. Here is our poetry, for we have pulled down the stars to our will.”
—Ezra Pound (1885–1972)