Polynomial regression fits a given data set to the following model:
where βi are the coefficients and 
is the error term. The error term represents the unexplained variation in the dependent variable. It is assumed that the mean of the random variable is equal to zero.
Parameters are estimated using a weighted least-square method. This method minimizes the sum of the squares of the deviations between the theoretical curve and the experimental points for a range of independent variables. After fitting, the model can be evaluated using hypothesis tests and by plotting residuals.
It is worth noting that the higher order terms in polynomial equation have the greatest effect on the dependent variable. Consequently, models with high order terms (higher than 4) are extremely sensitive to the precision of coefficient values, where small differences in the coefficient values can result in a larges differences in the computed y value. We mention this because, by default, the polynomial fitting results are rounded to 5 decimal places. If you manually plug these reported worksheet values back into the fitted curve, the slight loss of precision that occurs in rounding will have a marked effect on the higher order terms, possibly leading you to conclude wrongly, that your model is faulty. If you wish to perform manual calculations using your best-fit parameter estimates, make sure that you use full-precision values, not rounded values. Note that while Origin may round reported values to 5 decimal places (or other), these values are only for display purposes. Origin always uses full precision (double(8)) in mathematical calculations unless you have specified otherwise. For more information, see Numbers in Origin in the Origin Help file.
Generally speaking, any continuous function can be fitted to a higher order polynomial model. However, higher order terms may not have much practical significance.