Linear models, generalized linear models, and nonlinear models are examples of parametric regression models because we know the function that describes the relationship between the response and ...

This short course will provide an overview of non-parametric statistical techniques. The course will first describe what non-parametric statistics are, when they should be used, and their advantages ...

Parametric CFD analysis lets users statistically determine the fluid flow around a device. Fluid dynamics is one of the primary engineering sciences used to design a wide variety of vehicles, machines ...

In the post-parametric era, one key challenge for architectural design is the acquisition, processing, and integration of data. Designers already have an enormous amount of computable data from ...

Today’s devices are required to pass thousands of parametric tests prior to being shipped to customers. A key challenge test engineers face, in addition to optimizing the number of tests they run on ...

Data really powers everything that we do. Research activities in the data science area are concerned with the development of machine learning and computational statistical methods, their theoretical ...

Parametric release and real-time testing use manufacturing data to ensure that products are made according to defined standards. PharmTech talks to Boehringer Ingelheim's Heribert Hausler about these ...