Jan 18, 2021  
2018-2019 Undergraduate Catalog 
2018-2019 Undergraduate Catalog [ARCHIVED CATALOG]

MATH 448 - Computational Statistics

3 Credit(s) | Lecture | Graded or pass/fail
Course can be counted for credit once

This course is an introduction to the fundamental ideas and techniques of statistical inference. The course demonstrates how and when to use statistical methods, explains the mathematical background behind them and illustrates them with case studies. Topics covered include the Central Limit Theorem, parameter estimation, confidence intervals, hypothesis testing, type I and II errors, power, significance level, p-value, likelihood ration tests, t-test, paired and 2-population t-tests, goodness-of-fit tests, chi-square tests, contingency tables, exact tests, nonparametric tests, ANOVA and regression models. Statistical software such as R, Matlab, or Python, will be used to analyze real-world data.

Enrollment Requirements:
Prerequisites: MATH 345  and MATH 447  and CS 110  or permission of instructor