This course focuses on the building of empirical models through data in order to predict, explain, and interpret scientific phenomena. The main focus will be on multiple regression as a technique for doing this. Through no fault of its own, regression analysis has perhaps the most used of all data analysis methods. We will study both the mathematics of regression analysis and its applications, including a discussion of the limits to such analyses. The applications will range from predicting the quality of a vintage of Bordeaux wine from the weather to forecasting stock prices, and will come from a broad range of disciplines. Format: lecture. Evaluation will be based primarily on performance on the project, homework, and exams. Prerequisites: Statistics 101 or 201, and Mathematics 105 and 211; or permission of instructor. No enrollment limit (expected: 10). This is a quantitative/formal reasoning course.