I recently applied multiple regression analysis to examine what drives arithmetic performance in a sample of 177 participants. Using a model that included sex, teacher stage, math anxiety, self-efficacy, and personality traits, regression helped isolate the key drivers of performance while controlling for overlapping effects. The strongest and most consistent predictor was **self-efficacy**, followed by a smaller but significant effect of sex. Interestingly, math anxiety, personality (neuroticism), and teacher stage were not significant once other factors were controlled for. The model explained about **17–20% of performance variation**, showing how regression can move beyond correlation to identify the most decision-relevant predictors in complex human data. This is a clear example of how regression modeling supports evidence-based decisions by highlighting what truly matters when multiple factors interact in real-world systems.