Objectives This study investigated the impact of heat on the risk of hospital admission due to a range of health conditions in England. Design We used records of over 4 million hospital admissions in ...

BACKGROUND: Hypertension induces structural and functional damage in multiple organs. Evidence of subclinical damage ...

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: This tutorial aims to provide an intuitive introduction to Gaussian process regression (GPR). GPR models have been widely used in machine learning applications due to their representation ...

Objective To investigate the influence of demographic and socioeconomic factors on the COVID-19 case-fatality rate (CFR) globally. Design Publicly available register-based ecological study. Setting ...

Abstract: Feature extraction and dimensionality reduction are important tasks in many fields of science dealing with signal processing and analysis. The relevance of these techniques is increasing as ...

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.

Predictive modeling is one of the most important tools in data science. It helps professionals use past data to forecast future outcomes and make data-driven decisions. Among the many modeling ...

This paper deals with the use of multiple linear regression to predict the viscosity of engine oil at 100 °C based on the analysis of selected parameters obtained by Fourier transform infrared ...

In today’s data-rich environment, business are always looking for a way to capitalize on available data for new insights and increased efficiencies. Given the escalating volumes of data and the ...

As the influencing force behind predictive analytics, regression analysis is crucial for various data-driven decision-making processes. For example, imagine you're planning a road trip and want to ...