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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

• Further reading

• Linear least squares

• Linear segmented regression

• Linear trend estimation

• Polynomial regression

• Regression dilution

• "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]