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
- You want to see the shape of a relationship before choosing a parametric model
- The relationship may change direction more than once
- Outliers are present and a robust smoother is helpful
When to use something else
- You need coefficients, tests or confidence intervals for a curve; use natural cubic spline regression instead
- There are several predictors to adjust for at once; use generalized additive models (GAM) instead
Assumptions and requirements
- The mean of the outcome is a smooth function of the predictor
- The span is suitable for the amount of data and curvature
- Observations are independent
- A single predictor (the first feature) is smoothed with local linear fits
What the analysis reports
- Fitted curve evaluated over the range of the predictor
- Span and number of robustness iterations
- Variance explained by the smoother and by a straight line
- Residual standard error
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.