Statistical inference is the process of using data analysis to draw conclusions about a population or process beyond the existing data. Inferential statistical analysis infers properties of a population by testing hypotheses and deriving estimates. For example, you might survey a representation of people in a region and, using statistical principles including simulation and probability theory, make certain inferences based on that sample. In this course, you will explore modern statistical concepts and procedures derived from a mathematical framework. This course is designed for advanced undergraduates, Masters students in statistics, and Doctoral students in STEM and other programs. You will develop a deep understanding of how statistics works which will prepare you for additional coursework in statistics. Assignments may require some computation in R programming language.

Students in this course will learn, the principles underlying statistical methods including sample vs population; how to implement inferential tasks including testing, estimation, confidence intervals; model selection and how to use models based on a few specific distributions, such as normal, binomial, Poisson.

Topics Include

  • Decision theory
  • Point and interval estimation
  • Tests of hypotheses
  • Neyman-Pearson theory
  • Bayesian analysis
  • Maximum likelihood
  • Large sample theory