MATH 3310 Mathematical Statistics

A mathematical foundation for the study of statistics. Topics include sampling, estimators, methods of estimation, confidence intervals, hypothesis testing, and analysis of variance. 

 

Credits

3

Prerequisite

MATH 3300

Typically Offered

Demorest Campus: even spring day

Student Learning Outcomes

Upon the completion of this course, students will be able to demonstrate the following outcome-based learning skills:

1. Explain the concepts of random sampling, statistical inference and sampling distribution, and state and use basic sampling distributions.
2. Describe the main methods of estimation and the main properties of estimators, and apply them. Methods include matching moments, percentile matching, and maximum likelihood, and properties include bias, variance, mean squared error, consistency, efficiency, and UMVUE.
3. Construct confidence intervals for unknown parameters, including the mean, differences of two means, variances, and proportions,
4. Test hypotheses. Concepts to be covered include Neyman-Pearson lemma, significance and power, likelihood ratio test, and information criteria. Tests should include for mean, variance, contingency tables, and goodness-of-fit.