Regression analysis is highly relevant to agricultural sciences since many of the factors studied are quantitative. Researchers have generally used polynomial models to explain their experimental ...

Logistic regression is a statistical method used to model binary outcome variables, such as whether a patient recovers or not, using a set of predictors. There are many competing methods for ...

Abstract: A noninvasive ultra-wide band (UWB) partial discharge technique was employed in high capacity motors diagnosis. Those motors are installed and operating in an oil pumping plant. The ...

Study objective: In social epidemiology, it is easy to compute and interpret measures of variation in multilevel linear regression, but technical difficulties exist in the case of logistic regression.

Study objective: This didactical essay is directed to readers disposed to approach multilevel regression analysis (MLRA) in a more conceptual than mathematical way. However, it specifically develops ...

Abstract: This paper describes Weibull data analytics in support of grid resilience as well as interpreting test and R&D results. The target was to develop a widely accessible spreadsheet IDA ...

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Dr. James McCaffrey of Microsoft Research presents a full-code, step-by-step tutorial on this powerful machine learning technique used to predict a single numeric value. A regression problem is one ...

Creative Commons (CC): This is a Creative Commons license. Attribution (BY): Credit must be given to the creator. Nowadays, frontiers among different sciences are revealed as diffuse, and as a ...

Norm scores are an essential source of information in individual diagnostics. Given the scope of the decisions this information may entail, establishing high-quality, representative norms is of ...

The Partial Least Square Regression (PLSR) method has shown admirable competence for predicting continuous variables from inter-correlated electrocorticography signals in the brain-computer interface.