Statistics Definitions > Lowess Smoothing

## Lowess Smoothing: Overview

LOWESS (Locally Weighted Scatterplot Smoothing), sometimes called LOESS (locally weighted smoothing), is a popular tool used in regression analysis that creates a smooth line through a timeplot or scatter plot to help you to see relationship between variables and foresee trends.

## What is Lowess Smoothing used for?

LOWESS is typically used for:

- Fitting a line to a scatter plot or time plot where noisy data values, sparse data points or weak interrelationships interfere with your ability to see a line of best fit.
- Linear regression where least squares fitting doesn’t create a line of good fit or is too labor-intensive to use.
- Data exploration and analysis in the social sciences, particularly in elections and voting behavior.

### Parametric and Non-Parametric Fitting

LOWESS, and least squares fitting in general, are **non-parametric** strategies for fitting a smooth curve to data points. “Parametric” means that the researcher or analyst assumes in advance that the data fits some type of distribution (i.e. the normal distribution). Because some type of distribution is assumed in advance, parametric fitting can lead to fitting a smooth curve that misrepresents the data. In those cases, non-parametric smoothers may be a better choice. Non-parametric smoothers like LOESS try to find a curve of best fit without assuming the data must fit some distribution shape. In general, both types of smoothers are used for the same set of data to offset the advantages and disadvantages of each type of smoother.

### Benefits of Non-Parametric Smoothing

- Provides a flexible approach to representing data.
- Ease of use.
- Computations are relatively easy.

### Disadvantages of Non-Parametric Smoothing

- Can’t be used to obtain a simple equation for a set of data.
- Less well understood than parametric smoothers.
- Requires the analyst to use a little guesswork to obtain a result.

Image: Kierano|Wikimedia Commons

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**Stephanie Glen**. "Lowess Smoothing in Statistics: What is it?" From

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