Instructor: Arman Oganisian
Textbook: Casella, G. and Berger, R.L. (2002). Statistical Inference (2nd Edition). Duxbury Press, Pacific Grove. ISBN-13 978-0-534-24312-8.
Description: This course provided an introduction to probability theory, mathematical statistics, and their application to biostatistics. Emphasis was placed on mathematical and probabilistic concepts that form the foundation for statistical inference. The course covered fundamental ideas of probability, some foundational probability models (i.e., normal, binomial, exponential and Poisson), sample and population moments, finite and approximate sampling distributions, point and interval estimation, as well as hypothesis testing. In addition to discussing a range of applications and practical modeling examples, R programming was used to conduct analyses and inference.
After developing probability theory from the ground up, we turn to inference: using data to learn about the unknown probability models that govern random biomedical phenomena.
Fall ‘21