Methods · Regression · Nonlinear regression

LOESS Smoothing Regression

LOESS smoothing regression estimates a smooth curve by fitting weighted local regressions in a moving neighbourhood of each point, with tricube weights and optional robustness iterations.

LOESS는 각 점 주변의 이웃 자료에 가중 국소회귀를 반복 적합해 매끄러운 추세 곡선을 그리는 비모수 평활 방법입니다.

I don't know what shape the relationship between two variables has; how can I draw a data-driven trend line?

두 변수 관계가 어떤 모양인지 모를 때 자료에서 추세선을 그리려면 어떻게 하나요?

LOESS fits a small weighted regression around each value of the predictor, using the nearest fraction of the data (the span), and joins the local fits into a smooth curve. It is mainly an exploratory tool for seeing the shape of a relationship, and it gives no single slope or p value.

When to use LOESS Smoothing Regression

When to use something else

Assumptions and requirements

What the analysis reports

How to report it

A LOESS smoother (span = [value]) showed that [outcome] rose with [predictor] up to about [value] and then levelled off; the smoother explained [value]% of the variance compared with [value]% for a straight line.

Related methods

Run LOESS Smoothing Regression on Quorant

Data you need
a table (CSV or Excel) with an outcome variable and predictor (feature) columns
Price
400 credits for one analysis up to 10,000 rows (complexity level 3); larger or wider data costs more. Optional methodologist review: 1800 credits.
Checking
Checked by property tests in every deployment
Before you pay
A free pre-analysis diagnosis checks your data and gives an exact quote; nothing is deducted until you approve it.

Method ID Q21024 · Reviewed 2026-10-25