1) Project managers face challenges in providing accurate cost and schedule estimates to stakeholders due to uncertainties. Applied statistics techniques can improve predictability in project performance. 2) Key techniques discussed are sensitivity analysis and criticality index to identify critical activities, and Monte Carlo analysis to model schedule uncertainty using probability distributions. 3) Monte Carlo analysis involves generating random inputs, running simulations of a parametric model, and analyzing results using statistics to obtain forecasts. These techniques provide a more informed basis for project estimates than single-point estimates.