Line fitting is the process of constructing a straight line that has the best fit to a series of data points.
Several methods exist, considering:
- Vertical distance: Simple linear regression
- Resistance to outliers: Robust simple linear regression
- Perpendicular distance: Orthogonal regression (this is not scale-invariant i.e. changing the measurement units leads to a different line.)
- Weighted geometric distance: Deming regression
- Scale invariant approach: Major axis regression This allows for measurement error in both variables, and gives an equivalent equation if the measurement units are altered.
See also
- Linear least squares
- Linear segmented regression
- Linear trend estimation
- Polynomial regression
- Regression dilution
Further reading
- "Fitting lines", chap.1 in LN. Chernov (2010), Circular and linear regression: Fitting circles and lines by least squares, Chapman & Hall/CRC, Monographs on Statistics and Applied Probability, Volume 117 (256 pp.).
Index of articles associated with the same name
This set index article includes a list of related items that share the same name (or similar names). If an internal link incorrectly led you here, you may wish to change the link to point directly to the intended article.
 | The present page holds the title of a primary topic, and an article needs to be written about it. It is believed to qualify as a broad-concept article. It may be written directly at this page or drafted elsewhere and then moved to this title. Related titles should be described in Line fitting, while unrelated titles should be moved to Line fitting (disambiguation). (May 2019) |  |