This page allows you to work out multiple polynomial regressions, also known as multi-polynomial regressions or multiple polynomial least squares fittings. For the relation between several variables, it finds the polynomial function that properly fits a given set of data points. The result is not necessarily the best possible, but usually it is a very good one and further improvement posibilities are 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. Remember data must consist of n+1 columns, x₁...x_n and y, to get the multiple polynomial regression y=a_m1x₁^m+...+a_mnx_n^m+...+a₁₁x₁+...+a_1nx_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.