Curve fitting

statistical approach for modeling the relationship between a scalar dependent variable and one or more explanatory variables

process of constructing a curve, or mathematical function, that has the best fit to a series of data points

thumb|300px|Figure 1.  The green line represents an overfitted model and the black line represents a regularized model. While the green line best follows the training data, it is too dependent on that data and is likely to have a higher error rate on new unseen data, illustrated by black-outlined dots, compared to the black line. thumb|300x300px|Figure 2.  Noisy (roughly linear) data is fitted to a linear function and a polynomial function. Although the polynomial function is a perfect fit, the linear function can be expected to generalize better: If the two functions were used to ex

thumb|400px|Example of one-dimensional data interpolation by kriging, with credible intervals. Squares indicate the location of the data. The kriging interpolation, shown in red, runs along the means of the normally distributed credible intervals shown in gray. The dashed curve shows a spline that is smooth, but departs significantly from the expected values given by those means.

linear regression model with a single explanatory variable

thumb | right | Simple exponential smoothing example. Raw data: mean daily temperatures at the Paris-Montsouris weather station (France) from 1960/01/01 to 1960/02/29. Smoothed data with alpha factor = 0.1. In statistics and image processing, to smooth a data set is to create an approximating function that attempts to capture important patterns in the data, while leaving out noise or other fine-scale structures/rapid phenomena. In smoothing, the data points of a signal are modified so individual points higher than the adjacent points (presumably because of noise) are reduced, and points that a

method for determining the concentration of a substance in an unknown sample

type of statistical model

set of equations with more equations than unknowns