MATH 425: Statistical Inference

Course Description

Statistical inference applies probability theory to draw conclusions about a population when given information only from a sample. Students will learn and apply inferential procedures derived within a rigorous mathematical framework. In this course the fundamentals of mathematical statistics are studied, building on knowledge of probability theory. This course will cover point estimators (method of moments, maximum likelihood, Bayesian), confidence intervals, hypothesis testing, regression, and some theory, such as sufficient statistics and the likelihood ratio test. Probability theory will be reviewed as needed, and a statistical software such as RStudio will be introduced and used. If time allows, further topics will be considered, such as simulation and nonparametric methods. After taking this course, students will possess a deep understanding of how statistics works under the hood and in the real world. A minimum grade of C- is required in the prerequisite. Students cannot receive credit for bothMATH 425andMATH 525. (W).