As feature sizes continue to shrink, traditional corner analysis, because of its limitations and pessimism [1], is being gradually supplanted by Statistical Static Timing Analysis (SSTA) as our major timing tool. Recent works [2-4] propose non-linear models handling Gaussian and non-Gaussian variation sources, which is a great progress relative to linear dependency on Gaussian process parameters presented in [5-6]. The authors of [7] propose a simple and practical approach of SSTA. After having analyzed impacts of process variations on delay and probable phenomena of circuit, the authors simplify the cells timing characterization, and the basic operations MAX/MIN for random variables. Following the thought of simplicity, we propose herein a path-based timing approach to propagate iteratively probability density function (PDF) by adopting conditional moments. With this probability concept, plus a model of input signal and the employment of active load, path delay distribution can be estimated taking into account input slope and output load variations. Furthermore, in a first attempt, we propose a technique to estimate cell-to-cell delay correlation which value depends on: (a) environmental parameters like V and T, (b) cell topology and polarity, and (c) input slope and output load values.