This course provides an introduction to principles, terminology, and strategies for statistical modelling with the linear model as initial framework for data analysis. The linear model is a modelling ...

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Abstract: With the advent of smart systems and smart IoT network all over the world leading to enormous amount of data generation; the right analysis and decision making based on the relevant data ...

Abstract: Flood prediction as we known is a important role in reducing the impacts of effective disasters . This paper says that a linear regression-based model is designed for forecasting flood ...

Uncover the hidden pitfalls of Excel regression and learn why Python is the key to unlocking clean, efficient data analysis.

Overview. Data transformations are a useful companion for parametric regression models. A well-chosen or learned transformation can greatly enhance the applicability of a given model, especially for ...

Support vector regression can predict numeric values effectively, and this article shows how to implement and train a kernel SVR model in C# using stochastic sub-gradient descent.

David Gerbing from the School of Business at Portland State University introduces lessR, a tool designed to facilitate professional-quality data visualizations and data analysis without programming re ...

Thomas J Catalano is a CFP and Registered Investment Adviser with the state of South Carolina, where he launched his own financial advisory firm in 2018. Thomas' experience gives him expertise in a ...

Aims To develop prediction models for identifying cases with poor visual outcomes after surgery for primary rhegmatogenous ...

Glucose-to-potassium ratio shows a J-shaped link with AKI after traumatic brain injury, with high levels predicting increased ...