Abstract: Assumptions play a pivotal role in the selection and efficacy of statistical models, as unmet assumptions can lead to flawed conclusions and impact decision-making. In both traditional ...

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

Generally speaking, there are two types of outcomes (i.e. response) in statistical analysis: continuous and categorical responses. Linear Models (LM) are one of the most commonly used statistical ...

Linear models, generalized linear models, and nonlinear models are examples of parametric regression models because we know the function that describes the relationship between the response and ...

where y i is the ith observed response value, x i is the ith vector of explanatory values, and 's are uncorrelated random variables with zero mean and a common variance. If the form of the regression ...

Cross-sectional genetic association studies can be analyzed using Cox proportional hazards models with age as time scale, if age at onset of disease is known for the cases and age at data collection ...

Statistical models predict stock trends using historical data and mathematical equations. Common statistical models include regression, time series, and risk assessment tools. Effective use depends on ...

Reporting quality in clinical research is critical for evidence-based medicine and reproducibility of clinical studies. Previous work has mostly focused on the reporting quality of clinical trials and ...