This probably means that there are a lot of solutions, each one giving almost the same mean least squares value (i.e. a lot of different equations are likely to fit the data with the same accuracy).
This often occurs when there is a too large number of parameters to be optimized compared to the number of experimental values as given data.
For example, with W = A + B(z1) + C(z1^2) + D(z1^3) + E(z2) + F(z2^2) + G(z2^3), you have 7 parameters to optimize, wich is a rather big number. If the expected shape is not simple (i.e. with several maximums and minimums) and/or if the data is scattered, a non ambiguous fitting will require an anormous experimental data.
If the available data is not large enough, it is necessary to have a smaller number of parameters. This will not be possible with so elementary functions as linerar, square, cubic, etc. It is then a difficult problem to conjecture a batch of only a small number of functions, each one more complicated than the preceeding simple ones. This requires a careful study of the data, observation and much trial and error working while testing some functions supposed to be convenient.