Regression modeling is essential in epidemiology for several reasons:
1. Quantifying Relationships: It allows researchers to quantify the strength and direction of associations between risk factors and health outcomes. 2. Adjusting for Confounders: It helps control for confounding variables that could bias the results, thus isolating the effect of the primary exposure of interest. 3. Prediction: It enables the prediction of disease risk based on various exposures and demographic factors. 4. Hypothesis Testing: It allows for testing of epidemiological hypotheses regarding the relationships between variables.
