Correlational predictive analysis primarily utilizes linear regression to establish a predictive model that forecasts one variable based on another. While correlation assesses the strength and direction of the relationship between two variables, linear regression goes further by enabling predictions through this established relationship (Laerd, n.d.). The core quantitative research design at Grand Canyon University (GCU) that uses linear regression is the correlational-predictive design. This approach examines data from a single group to predict one variable based on another. Simple linear regression involves using the relationships between variables to make predictions. Cardoso-Pulido et al. (2022) argue that multiple linear regression helps researchers identify the strength and direction of associations between variables, demonstrating that positive psychological factors significantly influence teaching success