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.

• 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]