Mathematical Statistics, II

Course Introduction

Mathematical Statistics is a two-term sequence that gives some mathematical underpinnings to the material covered in the typical introductory statistics course. Traditionally, the first term pays attention to several distributions — both natural and test — and the Central Limit Theorem. That is the mathematical probability course, MATH 321.

The second term, MATH 322, traditionally focuses on estimators, confidence intervals, hypothesis testing, and several important applications of these three topics: goodness-of-fit, linear regression, and analysis of variance. This course is very traditional in the topics covered. It is modern, however, in some of the methods for teaching those topics. To help with the experimental aspects of these topics, especially with examining the effects of assumption violation, simulation is heavily used. The strength of using simulation is that it is quite easy to obtain approximate solutions, especially for complicated estimators, such as the midpoint estimator of the population mean μ.

Course Learning Modules

To make things easier for you, here are links to the the modules covered in this course. Please access them from here. Also, as with all courses, make sure you are familiar with the syllabus and the schedule.

Part O: Review

Part I: Theory

Part II: Application