Overview
Description
Statistical models and applications; estimation; unbiasedness, sufficiency, completeness; hypothesis testing: likelihood ratio test, Neyman-Pearson Lemma, most powerful tests; linear models; special topics.
Units
Lecture3
Catalog Details
Offering
Offered: Every Spring
Terms
spring
Attributes
Standard
Learning Outcomes
- demonstrate advanced level of competency in building, analizing, and interpreting statistical methods of frequentist and Bayes estimation, and linear modeling, combining analytical and numerical approaches.
- demonstrate advanced level of competency in hypothesis testing by implementing and critically assessing the assumptions and results of the likelihood ratio techniques, and test power, combining analyticall and computational approaches.
- perform statistical analysis using a professional Statistical package, prepare reports, and present their results to a professional audience.