This fits to the data, a polynomial function of the form:

y = b0+ b1x + b2x2 + b3x3 +...+ bnxn

where n is the order of the polynomial (1<n<6). The order of the polynomial function can be changed in the Fitting dialog.

Fitting centered polynomial models via Multiple Regression. The fitting of higher-order polynomials of an independent variable with a mean not equal to zero can create difficult numerical problems. Specifically, the polynomials will be highly correlated due to the mean of the primary independent variable. With large numbers (e.g., Julian dates), this problem is very serious, and if proper protections are not put in place, can cause wrong results. The solution is to "center" the independent variable (sometimes, this procedures is referred to as "centered polynomials"), i.e., to subtract the mean, and then to compute the polynomials. See, for example, the classic text by Neter, Wasserman, & Kutner (1985, Chapter 9), for a detailed discussion of this issue (and analyses with polynomial models in general). Note that the Multiple Regression method of analysis will automatically check for very large numbers (created in the process of computing the polynomials), and issue a warning message to alert the user of potential multicollinearity problems.