Several examples of multivariate techniques implemented in R, Python, and SAS. ... andiosika / Multiple-linear-regression-for-predicting-home ... Using 21 categorical and numeric features in a ...

In most of the manufacturing processes, we encounter different quality characteristics of a product and process. These characteristics can be categorized into two kinds; study variables (variable of ...

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Semidefinite programming (SDP) is an important branch of optimization and has wide range of applications: engineering, industry, chemistry, mathematics, etc. However, obtaining very accurate optimum ...

A multiple polynomial mathematical model, which simultaneously considers metabolizable ... and vitamin-mineral premix US$ 2.56/kg. The simplex algorithm, a linear programming technique, was adopted to ...

Perfluoroalkyl and polyfluoroalkyl substances (PFAS) are used in many applications due to their attractive nonstick and fire-fighting properties. When released into aquatic environments, commercial ...

The diagram in Figure 2 gives you a rough idea of support vector regression for a scenario where there is just one predictor variable x. Each dot is a training data item. The red line is the linear ...

Implementing binary / multiple logistic regression models, for the well known mnist dataset while also creating the support vector machine(SVM) models . support-vector-machines radial-basis-function ...

Self-powered catalytic micromotors that swim through a fluid by harvesting energy from their environment have received increasing attention because of their potential applications in nanomachinery, ...

The objectives of this work were to predict soil properties using pXRF data, comparing stepwise multiple linear regression (SMLR) and random forest (RF) methods, as well as mapping and validating soil ...