Stepwise Regression

Stepwise regression is a statistical method used to select the most relevant variables for predicting an outcome while controlling for others. It starts with a full model that includes all potential predictors. Then, it iteratively adds or removes variables based on their statistical significance in improving the model. This process continues until the best combination of variables is identified, balancing complexity and accuracy. In essence, stepwise regression helps in building simpler models that still capture important relationships in the data, making it easier to understand and apply in various fields like economics, healthcare, and social sciences.