The adjusted r-squared is helpful for multiple regression and corrects for erroneous regression, giving you a more accurate ...

(1) Data from a survey of sites where Helianthemum chamaecistus was common were analysed to identify the factors which might control the occurrence of the plant. (2) The main patterns in the plant ...

Multiple regression models are commonly used to control for confounding in epidemiologic research. Parametric regression models, such as multiple logistic regression, are powerful tools to control for ...

Transcriptomic and clinical data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus were used to construct a 12-gene ARG-based prognostic signature through LASSO and Cox regression ...

Their study is centred around answering three research questions: Do ANNs perform better than the traditional multiple regression models in the prediction of lighting parameters and energy demand of ...