Statistics 490S - Math Foundatns/Stat & Data Sci

In this course, we see how probability theory is used in statistics to formalize and facilitate the process of learning from data, which is called statistical inference. The three main aspects of statistical inference we will cover are point estimation, confidence intervals, and hypothesis tests. For each of these topics, we will provide the goals and motivation, basic definitions, general methodology, and commonly used tools, including inference for means, differences in means, proportions, and differences in proportions. The course is structured in five basic sections. First, we will discuss point estimators of statistical parameters and their properties. Second, we will introduce several of the most important methods of estimation: minimum variance unbiased estimators, method of moments, and maximum likelihood estimation. Third, we will discuss the construction of confidence intervals, including pivotal intervals in parametric models and approximate intervals for use in large samples. Fourth, we will introduce hypothesis testing, and develop hypothesis tests for means and proportions. Fifth, we will introduce Bayesian inference. We will learn how to use the statistical programming software R to implement all methods covered in the class. This class has 2.5 hours of lecture time per week and 50 minutes of required recitation section time per week.

STATISTC 315 This course has 2.5 hours of lecture time per week and 50 min. of required recitation section time per week. Recitation sections will be run by TAs, and will cover details of programming in R and additional worked examples of material covered in lecture.

This course satisfies all the requirement of STAT 516.

Seats reserved for Math majors or Stat and DS majors