Regression models are essential in epidemiology for several reasons:
Risk Assessment: They help identify and quantify the effect of risk factors on health outcomes. Prediction: These models can forecast future disease trends based on current data. Causal Inference: They help establish causal relationships between exposures and outcomes. Control of Confounding: Regression models can adjust for confounding variables, providing a clearer picture of the relationship between the primary exposure and outcome.
