In statistics, polynomial regression is a form of regression analysis in which the relationship between the independent variable x and the dependent variable y is modelled as an nth degree polynomial in x. Polynomial regression fits a nonlinear relationship between the value of x and the corresponding conditional mean of y, denoted E(y |x). 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 goal of regression analysis is to model the expected value of a dependent variable y in terms of the value of an independent variable (or vector of independent variables) x. In simple linear regression, the model
{\displaystyle y=\beta _{0}+\beta _{1}x+\varepsilon ,\,}{\displaystyle y=\beta _{0}+\beta _{1}x+\varepsilon ,\,}
is used, where ε is an unobserved random error with mean zero conditioned on a scalar variable x. In this model, for each unit increase in the value of x, the conditional expectation of y increases by β1 units.
In many settings, such a linear relationship may not hold. For example, if we are modeling the yield of a chemical synthesis in terms of the temperature at which the synthesis takes place, we may find that the yield improves by increasing amounts for each unit increase in temperature. In this case, we might propose a quadratic model of the form
{\displaystyle y=\beta _{0}+\beta _{1}x+\beta _{2}x^{2}+\varepsilon .\,}{\displaystyle y=\beta _{0}+\beta _{1}x+\beta _{2}x^{2}+\varepsilon .\,}
In this model, when the temperature is increased from x to x + 1 units, the expected yield changes by {\displaystyle \beta _{1}+\beta _{2}(2x+1).}{\displaystyle \beta _{1}+\beta _{2}(2x+1).} (This can be seen by replacing x in this equation with x+1 and subtracting the equation in x from the equation in x+1.) For infinitesimal changes in x, the effect on y is given by the total derivative with respect to x: {\displaystyle \beta _{1}+2\beta _{2}x.}{\displaystyle \beta _{1}+2\beta _{2}x.} The fact that the change in yield depends on x is what makes the relationship between x and y nonlinear even though the model is linear in the parameters to be estimated.
In general, we can model the expected value of y as an nth degree polynomial, yielding the general polynomial regression model
{\displaystyle y=\beta _{0}+\beta _{1}x+\beta _{2}x^{2}+\beta _{3}x^{3}+\cdots +\beta _{n}x^{n}+\varepsilon .\,}{\displaystyle y=\beta _{0}+\beta _{1}x+\beta _{2}x^{2}+\beta _{3}x^{3}+\cdots +\beta _{n}x^{n}+\varepsilon .\,}
Conveniently, these models are all linear from the point of view of estimation, since the regression function is linear in terms of the unknown parameters β0, β1, .... Therefore, for least squares analysis, the computational and inferential problems of polynomial regression can be completely addressed using the techniques of multiple regression. This is done by treating x, x2, ... as being distinct independent variables in a multiple regression model.
Although polynomial regression is technically a special case of multiple linear regression, the interpretation of a fitted polynomial regression model requires a somewhat different perspective. It is often difficult to interpret the individual coefficients in a polynomial regression fit, since the underlying monomials can be highly correlated. For example, x and x2 have correlation around 0.97 when x is uniformly distributed on the interval (0, 1). Although the correlation can be reduced by using orthogonal polynomials, it is generally more informative to consider the fitted regression function as a whole. Point-wise or simultaneous confidence bands can then be used to provide a sense of the uncertainty in the estimate of the regression function.