Because it is so computationally intensive, LOESS would have been practically impossible to use in the era when least squares regression was being developed. The trade-off for these features is increased computation. In fact, one of the chief attractions of this method is that the data analyst is not required to specify a global function of any form to fit a model to the data, only to fit segments of the data.

It does this by fitting simple models to localized subsets of the data to build up a function that describes the deterministic part of the variation in the data, point by point. LOESS combines much of the simplicity of linear least squares regression with the flexibility of nonlinear regression. They address situations in which the classical procedures do not perform well or cannot be effectively applied without undue labor. LOESS and LOWESS thus build on 'classical' methods, such as linear and nonlinear least squares regression. In some fields, LOESS is known and commonly referred to as Savitzky–Golay filter (proposed 15 years before LOESS). They are two strongly related non-parametric regression methods that combine multiple regression models in a k-nearest-neighbor-based meta-model. Its most common methods, initially developed for scatterplot smoothing, are LOESS ( locally estimated scatterplot smoothing) and LOWESS ( locally weighted scatterplot smoothing), both pronounced / ˈ l oʊ ɛ s/. Local regression or local polynomial regression, also known as moving regression, is a generalization of the moving average and polynomial regression. The LOESS curve approximates the original sine wave.

LOESS curve fitted to a population sampled from a sine wave with uniform noise added. ( June 2011) ( Learn how and when to remove this template message) Please help to improve this article by introducing more precise citations. This article includes a list of general references, but it lacks sufficient corresponding inline citations.

Non linear vs linear scatter plot