Discover the importance of homoskedasticity in regression models, where error variance is constant, and explore examples that illustrate this key concept.

Objective Cardiovascular diseases (CVD) remain the leading cause of mortality globally, necessitating early risk identification to improve prevention and management strategies. Traditional risk ...

“You are going to die young.” The first time I heard those six words, they were jarring. And I chuckle when I hear them.

Machine learning for health data science, fuelled by proliferation of data and reduced computational costs, has garnered ...

In order to understand currents, tides and other ocean dynamics, scientists need to accurately capture sea surface height, or a snapshot of the ocean's surface, including peaks and valleys due to ...

Researchers have created a prediction method that comes startlingly close to real-world results. It works by aiming for strong alignment with actual values rather than simply reducing mistakes. Tests ...

Prediction markets have moved from academic curiosities to regulated financial venues, but the regulatory environment that governs them is still evolving. Wealth management executives now face a new ...

Mathematicians may have a better way to measure agreement across different datasets. Agreement affects reproducibility, meta-analysis, and prediction to fill in missing data points. We need a more ...

In microbiome studies, addressing the unique characteristics of sequence data—such as compositionality, zero inflation, overdispersion, high dimensionality, and non-normality—is crucial for accurate ...

Cardiovascular-kidney-metabolic (CKM) syndrome is a novel construct recently defined by the American Heart Association in response to the high prevalence of metabolic and kidney disease.

Cryptographic hash functions secure data by providing a unique fixed-length output for each input. These functions are essential in blockchain for data integrity and secure transactions. Understanding ...