Covers simple linear regression, multiple regression, and analysis of variance models. Students learn to use the software package R to perform the analysis, and to construct a clear technical report on their analysis, readable by either scientists or nontechnical audiences. (Formerly AMS 156.)
Introduction to probability theory and its applications. Combinatorial analysis, axioms of probability and independence, random variables (discrete and continuous), joint probability distributions, properties of expectation, Central Limit Theorem, Law of Large Numbers, Markov chains. Students cannot receive credit for this course and STAT 203 and
CMPE 107. (Formerly AMS 131.)
Instructor
The Staff, Raquel Prado, Athanasios Kottas, Bruno Sanso, Yonathan Katznelson, David Draper, Ju Hee Lee
General Education Code
SR
Quarter offered
Fall, Winter, Spring, Summer
Introduction to statistical inference at a calculus-based level: maximum likelihood estimation, sufficient statistics, distributions of estimators, confidence intervals, hypothesis testing, and Bayesian inference. (Formerly AMS 132.)
Instructor
The Staff, Raquel Prado, Athanasios Kottas, David Draper, Ju Hee Lee
General Education Code
SR
Quarter offered
Winter, Spring
Students submit petition to sponsoring agency.
Students submit petition to sponsoring agency.