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use a polynomial fit equation to predict new dataI need to create a new column that uses a calculated "Polynomial Fit Degree=4" equation to predict a value.

I have 97 different sites with monthly average temperature data. Therefor 97 different figures with a satisfactory line drawn through the 12 data points. Here is an example of one site's equation:

T_AVE = 2.127629 + 0.0769089*DOY - 0.0015991*(DOY-183.083)^2 - 2.3911e-6*(DOY-183.083)^3 + 2.1454e-8*(DOY-183.083)^4

I want to create a new column that will predict the X variable "DOY" when the Y variable "T_AVE" equals 5. There should be two solutions (~120, ~320).

Do I need to copy (by hand) the equation for each of the 97 sites and paste it into a new column?

Any suggestions will be appreciated.Re: use a polynomial fit equation to predict new dataYou don't need to copy the formula by hand: the Fit Model procedure can do that for you. In the following, I've opened up the Big Class data set, added three rows to the bottom of it and inserted into them three heights for which I want to predict the corresponding weight. Since these rows don't have a corresponding weight (y) value, the extra heights I've added won't affect the equation I get from the regression that I'm about to run on the whole table.

I've then fitted a cubic polynomial in height to the weight data: originally I did this interactively, but I've copied the resulting script below (to do that, click on the red triangle at the top of the window and select Script | Save Script to Script Window), so if you run this you should see the resulting output from that model fit: