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.
A rigorous introductory probability course, covering basic combinatorics, discrete and continuous random variables, special distributions, laws of large numbers and central limit theorem. In addition, advanced calculus: familiarity with infinite series, limits and double integrals.
A conferred Bachelor’s degree with an undergraduate GPA of 3.3 or better.
The course schedule is displayed for planning purposes – courses can be modified, changed, or cancelled. Course availability will be considered finalized on the first day of open enrollment. For quarterly enrollment dates, please refer to our graduate education section.