Upper-Division

STAT 108 Linear Regression

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.)

Credits

5

Instructor

Herbert Lee

Requirements

Prerequisite(s): STAT 132 and satisfaction of the Entry Level Writing and Composition requirements.

STAT 131 Introduction to Probability Theory

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 course 203 and Computer Engineering 107. (Formerly AMS 131.)

Credits

5

Instructor

The Staff, Raquel Prado, Athanasios Kottas, Bruno Sanso, Jonathan Katznelson, David Draper, Ju Hee Lee

Requirements

Prerequisite(s): AM 11B or ECON 11B or MATH 11B or MATH 19B or MATH 20B.

General Education Code

SR

Quarter offered

Fall, Winter, Spring

STAT 132 Classical and Bayesian Inference

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.)

Credits

5

Instructor

The Staff, Raquel Prado, Athanasios Kottas, David Draper, Abel Rodriguez, Ju Hee Lee

Requirements

Prerequisite(s): STAT 131 or CSE 107.

General Education Code

SR

Quarter offered

Winter, Spring

STAT 198 Independent Study or Research

Students submit petition to sponsoring agency.

Credits

5

STAT 198F Independent Study or Research

Students submit petition to sponsoring agency.

Credits

2