Description

Rigorous derivation of statistical results, clarification of limitations of statistical analysis, extensive use of computational software, application of statistical methods to case studies. Topics include: Graphical and numerical techniques for exploring data. Use and accuracy of population samples using parametric and nonparametric methods. Determination of probability distributions from statistical data. Use of computational methods based on resampling of data to determine reliability of statistical information. Classical statistical inference methods: probability distribution estimation, confidence intervals for statistical results, hypothesis testing for statistical significance. Fitting of data using linear regression and determining the accuracy of fit. Bayesian methods for estimating probability distributions using prior information. Advanced topics such as importance sampling for understanding the probability of rare events.

Credits

4

Recent Professors

Schedule Planner

Recent Semesters

Spring 2019

Offered

TuTh, F

Avg. Class Size

30

Avg. Sections

2