With technology scaling, the variability of device parameters continues to increase. This impacts both the performance and the temperature profile of the die turning them into a statistical distribution. To the best of our knowledge, no one has considered the impact of the statistical thermal profile during statistical analysis of the propagation delay. We present a statistical static timing analysis (SSTA) tool which considers this interdependence and produces accurate timing estimation. Our average errors for mean and standard deviation are 0.95% and 3.5% respectively when compared against Monte Carlo simulation. This is a significant improvement over SSTA that assumes a deterministic power profile, whose mean and SD errors are 3.7% and 20.9% respectively. However, when considering 90% performance yield, our algorithm's accuracy improvement was not as significant when compared to the deterministic power case. Thus, if one is concerned with the runtime, a reasonable estimate of the performance yield can be obtained by assuming nominal power. Nevertheless, a full statistical analysis is necessary to achieve maximum accuracy.