This course aims to teach students to use programming to gain intuition about statistical theory and fundamental concepts and to visualize data appropriately. Specifically, computational methods covered include simulation methods and numerical methods in maximization and integration. Appropriate graphical displays of statistical and simulation results will be emphasized. Statistical concepts covered include sampling distributions, confidence intervals and p-values, the central limit theorem, expectation, and maximum likelihood estimation. Student understanding of course ideas will rely heavily on performing simulation studies and discussing the assimilated class results online.

This course aims to teach students to use programming to gain intuition about statistical theory and fundamental concepts and to visualize data appropriately. Specifically, computational methods covered include simulation methods and numerical methods in maximization and integration. Appropriate graphical displays of statistical and simulation results will be emphasized. Statistical concepts covered include sampling distributions, confidence intervals and p-values, the central limit theorem, expectation, and maximum likelihood estimation. Student understanding of course ideas will rely heavily on performing simulation studies and discussing the assimilated class results online.