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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.). [1]
- "Homogeneous Least-Squares Problem", Keijo Inkilä (2005), The Photogrammetric Journal of Finland, 19(2):34–42
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