This page allows performing multiple polynomial regressions (multi-polynomial regressions, multiple polynomial least squares fittings). For the relation between several variables, it finds the polynomial function that best fits a given set of data points. The result can have a small -usually insignificant- deviation from optimality, but usually it is very good and further improvement possibilities are very small. In the case that the number of unknowns is equal to the number of data points a multivariate polynomial interpolation results.

• Copy & Paste: You can copy and paste data directly from a spreadsheet or a tabulated data file in the box below. Any character that cannot be part of a number -space, comma, tabulation...- is considered a column separator. By default commas are considered column separators; in the case you are using them as decimal separators check the option below. The exponent can be indicated by preceding it by the character E or e, as you can see in the example. Data must consist of n+1 columns, x_{1}...x_{n} and y, to get the multiple polynomial regression y=a_{m1}x_{1}^{m}+...+a_{mn}x_{n}^{m}+...+a_{11}x_{1}+...+a_{1n}x_{n}+b._{ }

• Insert manually & See details: If you prefer you can insert all the points manually, for which you first have to specify the number of data points. You also can see details of the calculation -as the calculated value of y and the error at each point- in this area.