Experiments usually aim to study how changes in various factors affect the response variable of interest. Since the response model used most often at present in experimental design is expressed through the effect of each factor, it is straightforward to ascertain how each factor affects the response variable. However, since the response model contains redundant parameters, in the analysis of variance we must calculate the degrees of freedom defined by the number of independent parameters. In this letter, we propose the idea of calculating the degrees of freedom over the model based on an orthonormal system for the first time. In this way, we can easily obtain the number of independent parameters associated with any component, which reduces the risk of mistakes in the calculation of the number of independent parameters and facilitates the implementation of estimation procedures.

We propose that the model in experimental design be expressed in terms of an orthonormal system. Then, we can easily estimate the effects using Fourier transforms. We also provide the theorems with respect to the sum of squares needed in analysis of variance. Using these theorems, it is clear that we can execute the analysis of variance in this model.