Multivariate statistical techniques are used extensively in metabolomics studies, ranging from biomarker selection to model building and validation. Two model independent variable selection techniques, principal component analysis and two sample t-tests are discussed in this chapter, as well as classification and regression models and model related variable selection techniques, including parti...

This introductory tutorial gives a survey on the use of statistical designs for what-if or sensitivity analysis in simulation. This analysis uses regression analysis to approximate the input/output transformation that is implied by the simulation model; the resulting regression model is also known as metamodel, response surface, compact model, emulator, etc. This regression analysis gives bette...

This introductory tutorial gives a survey on the use of statistical designs for what-if or sensitivity analysis in simulation. This analysis uses regression analysis to approximate the input/output transformation that is implied by the simulation model; the resulting regression model is also known as metamodel, response surface, compact model, emulator, etc. Regression analysis gives better res...

Statistical techniques play a major role in contemporary methods for analyzing magnetic resonance imaging (MRI) data. In addition to the central role that classical statistical methods play in research using MRI, statistical modeling and machine learning techniques are key to many modern data analysis pipelines. Applications for these techniques cover a broad spectrum of research, including man...

This study examined various regression-based techniques and an artificial neural network used for streamflow forecasting during typhoons. A flow hydrograph was decomposed into two segments, rising and falling limbs, and the individual segments were modeled using statistical techniques. In addition, a conceptual rainfallerunoff model, namely the Public Works Research Institute (PWRI)-distributed...