Multicollinearity is a common issue in multiple regression analysis, where the presence of high correlation among predictor ...

Objectives There is a lack of knowledge about whether occupational exposures increase the risk of emphysema, especially in ...

If you have experience with R or want a quick way to generate a regression with statsmodels using a pandas DataFrame, you can ...

I 'm a big fan of Python for data analysis, but even I get curious about what else is available. R has long been the go-to language for statistics, but the "Tidyverse" has given the language a serious ...

Abstract: Heavy rainfall prediction is crucial for various applications such as flood forecasting, water resource management, and agriculture. In this study, we propose a multi linear regression ...

Karen Mak brings you a bumper edition blog on social prescribing, finding that rates have steadily grown recently in the UK, ...

Abstract: A revolutionary area of artificial intelligence called machine learning enables computers to learn from data and forecast without the need for explicit programming. A key component of ...

The study is useful for advancing spatial transcriptomics through its novel regression-based linear model (glmSMA) that integrates single-cell RNA-seq with spatial reference atlases, and its ...