The paper presents a unified approach to the statistical linearization of input/output mapping of non-linear discrete-time stochastic systems driven with white-noise Gaussian process. The approach is concerned with a possibility of applying any consistent measures of dependence (that is those measures of dependence of a pair of random values, which vanish if and only if these random values are stochastically independent) in statistical linearization problems and oriented to the elimination of drawbacks concerned with applying correlation and dispersion (based on the correlation ratio) measures of dependence, based on linearized representations of their input/output models.