This course introduces students to the analytical tools needed to establish the properties of common statistical methods. We will answer questions such as: "Why do sampling distributions of many statistics become bell-shaped with large sample sizes?" "When is the sample mean the best estimator for the population mean?" and "What do we mean by "best" estimator?"
While building a proper foundation for inference, we emphasize the application of theory throughout the course. The course makes use of computing as a bridge between classical theory and the modern approaches used to characterize statistical methods.
This class will help prepare students to engage in statistical literature at an introductory level. Particularly, this class is aimed toward students considering graduate-level work in statistics. It does require a firm grasp of probability.
As in any statistics course, we emphasize statistical literacy and reasoning. This class also emphasizes the underlying theory of statistical methodology. Specifically, after taking this course, you will be able to: