REGRESSION MODELING IN COMPUTER-SUPPORTED LEARNING ENVIRONMENTS

We consider the role of technology in learning concepts of modeling univariate functional dependencies. It is argued that simple scatter plot smoothers for univariate regression problems are intuitive concepts that- beyond their intended usefulness in providing a possible answer to more intricate regression problem - may serve as a paradigm for statistical thinking, detecting structure in noisy data. Simulation may play a decisive role in understanding the underlying concepts and acquiring insight into the relationship between structural and random variation.