Integrated Risk Analytics for Cost and Schedule or Joint Confidence Level (JCL) analysis has proven to be successful for NASA. Bottom-up resource-loaded schedules are the most common method for jointly analyzing cost and schedule risk. However, the use of high-level parametrics and machine learning has been successfully used by one of the authors. This approach has some advantages over the more detailed method. In this presentation, we discuss the use of parametrics and machine learning methods. The parametric/machine learning approach involves the development of mathematical models for cost and schedule risk. Parametric methods for cost typically use linear and nonlinear regression analysis. These methods applied to schedule often do not provide the high R-squared values seen in cost models. We discuss the application of machine learning models, such as regression trees, to develop higher-fidelity schedule models. We then introduce a bivariate model to combine the results of the cost and schedule risk analyses, along with correlation, to create a JCL using models for cost and schedule as inputs. We provide a previous case study of the successful use of this approach for a completed spacecraft mission and apply the approach to a large data set of cost, schedule, and technical information for software projects.