A research team at Duke University has developed a new AI framework that can uncover simple, understandable rules that govern some of the most complex dynamics found in nature and technology. The AI ...

Linear functions are used to model a broad range of real-world problems. The ability to solve linear equations and inequalities is an essential skill for analysing these models. This section covers ...

Linear graphs are straight-line graphs that visually represent a constant rate of change in the relationship between two variables, showing how one changes in response to the other. They are expressed ...

Proper handling of continuous variables is crucial in healthcare research, for example, within regression modelling for descriptive, explanatory, or predictive purposes. However, inadequate methods ...

Linear regression is a powerful and long-established statistical tool that is commonly used across applied sciences, economics and many other fields. Linear regression considers the relationship ...

Abstract: Variable selection for genome-wide association studies is a key focus for bioinformatics researchers in high-performance computing. The rapid growth of biological and biomedical data demands ...

Students graph linear equations in Zach Loy’s ninth-grade algebra class at Braham Area High School in Minnesota. Credit: Patience Zalanga for The Hechinger Report The Hechinger Report covers one topic ...

An illustration of a magnifying glass. An illustration of a magnifying glass.

Microsoft Excel can perform numerous statistical functions, as well as create charts from statistical data. Statistical data includes numeric variables or word variables, also called categorical ...

Graphing an equation is an essential skill in mathematics, as it helps us visualize the relationships between variables. There are several techniques that can be employed to make graphing easier and ...

The emergence of deep learning has not only brought great changes in the field of image recognition, but also achieved excellent node classification performance in graph neural networks. However, the ...