When characterizing solutions of random coil polymers by static light scattering (SLS) or dynamic light scattering (DLS), linear regression is used to fit experimental data to theoretical relationships. These relationships are expressed as polynomial equations, which contain two independent variables-sample concentration and scattering angle-and a response or dependent variable that is related to radiation intensities (SLS) or intensity fluctuations (DLS). The coefficients of the terms in the polynomial are used to estimate parameters such as molecular weight and polymer coil radius of gyration. One major problem during data analysis involves deciding which polynomial model is appropriate for use with the data that inherently contains a high level of random noise that is produced by the presence of dust in the solutions. Dust is an especially troublesome problem when dealing with large polymers in aqueous solutions. Polynomial models having more terms than justified are unacceptable because the coefficients of these models are excessively corrupted by the noise. Thus, conclusions from unjustified models can be erroneous. This article discusses use of a factorial experimental design technique that obtains an acceptable model for fitting light scattering data containing high levels of random noise.