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polynomial regression
In statistics, polynomial regression is a form of linear regression in which the relationship between the independent variable ''x'' and the dependent variable ''y'' is modelled as an ''n''th degree polynomial. Polynomial regression fits a nonlinear relationship between the value of ''x'' and the corresponding conditional mean of ''y'', denoted E(''y'' | ''x''), and has been used to describe nonlinear phenomena such as the growth rate of tissues, the distribution of carbon isotopes in lake sediments, and the progression of disease epidemics. Although ''polynomial regression'' fits a nonlinear model to the data, as a statistical estimation problem it is linear, in the sense that the regression function E(''y'' | ''x'') is linear in the unknown parameters that are estimated from the data. For this reason, polynomial regression is considered to be a special case of multiple linear regression.
The predictors resulting from the polynomial expansion of the "baseline" predictors are known as interaction features. Such predictors/features are also used in classification settings.
== History ==
Polynomial regression models are usually fit using the method of least squares. The least-squares method minimizes the variance of the unbiased estimators of the coefficients, under the conditions of the Gauss–Markov theorem. The least-squares method was published in 1805 by Legendre and in 1809 by Gauss. The first design of an experiment for polynomial regression appeared in an 1815 paper of Gergonne. In the twentieth century, polynomial regression played an important role in the development of regression analysis, with a greater emphasis on issues of design and inference. More recently, the use of polynomial models has been complemented by other methods, with non-polynomial models having advantages for some classes of problems.
抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』
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