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
In statistics, simple linear regression (SLR) is a linear regression model with a single explanatory variable. That is, it concerns two-dimensional sample points with one independent variable and one dependent variable (conventionally, the ''x ...
*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
Regression dilution, also known as regression attenuation, is the biasing of the linear regression slope towards zero (the underestimation of its absolute value), caused by errors in the independent variable.
Consider fitting a straight line ...
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.)
{{Set index article
Regression analysis
Geometric algorithms