Dunwell, Rachel M.2013-03-072013-03-072011-01-12http://hdl.handle.net/10267/15780This syllabus was submitted to the Office of Academic Affairs by the course instructor. Uploaded by Archives RSA Josephine Hill.Statistics is widely considered an exciting, dynamic, and intrinsically inter- disciplinary science. The work of statisticians powers search engines like Google, has proven critical to the exploration of the human genome, and is used by hedge fund managers to detect arbitrage opportunities (risk-free trading strategies that yield pro t with positive probability) that are prof- itable only on average (called statistical arbitrage). The New York Times recently declared that, over the next decade, statisticians will enjoy one of the highest-paying, highly-coveted careers. Statistics is often considered a mathematical science quite distinct from mathematics itself. It arguably began in the 17th century with the development of probability theory by Blaise Pascal and Pierre de Fermat. Probability theory itself arose due to interest in games of chance. In contrast to probability theorists (who propose probability models and then study those models with somewhat less regard for the particular random realizations generated by those models), statisticians are interested in the random realizations themselves (called data), and what those random realizations suggest about the parameters that govern the underlying probability models. A critical development in the history of statistics was the method of least squares, which was probably rst described by Carl Friedrich Gauss in 1794. Early applications of statistical thinking revolved around the needs of states to base public policy on demographic, economic, and public health data. The scope of the discipline of statistics broadened in the early 19th century to include the collection and analysis of data in general. Today, statistics is widely employed in government, business, and the natural and social sciences. Computers are transforming the eld at a breathtaking pace. In fact, this semester, our approach to the two main tasks of statistical inference|constructing con dence intervals and executing hypothesis tests|will be motivated by simulations and visualizations in a software environment. Please be aware that there can ultimately be no escape from approaching statistics in this fashion. Because hard drive space is becoming much cheaper (i.e., it is easy to collect and store vast quantities of data) and processing speeds are becoming much faster (i.e., it is easy to do more things with data than ever before), the world of tomorrow will be dominated by the computer-driven data analysis we will undertake this semester!en-USRhodes College owns the rights to the archival digital objects in this collection. Objects are made available for educational use only and may not be used for any non-educational or commercial purpose. Approved educational uses include private research and scholarship, teaching, and student projects. For additional information please contact archives@rhodes.edu. Fees may apply.SyllabusCurriculumAcademic departmentsTextMathematics and Computer Science, Department of2011 SpringSyllabus