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

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.).