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
- Weighted geometric distance: Deming regression
- Scale invariance: Major axis regression
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Line Fitting, Residuals, and Correlation
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HP 35s Linear Regression (Line Fitting)
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Fitting a Line with Least Squares Regression
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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]
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This page was last edited on 30 October 2021, at 07:07