STATS 32: Introduction to R for Undergraduates
This short course runs for weeks one through five of the quarter. It is recommended for undergraduate students who want to use R in the humanities or social sciences and for students who want to learn the basics of R programming. The goal of the short course is to familiarize students with R's tools for data analysis. Lectures will be interactive with a focus on learning by example, and assignments will be application-driven. No prior programming experience is needed. Topics covered include basic data structures, File I/O, data transformation and visualization, simple statistical tests, etc, and some useful packages in R. Prerequisite: undergraduate student. Priority given to non-engineering students. Laptops necessary for use in class.
Terms: Aut, Spr
| Units: 1
STATS 60: Introduction to Statistical Methods: Precalculus (PSYCH 10, STATS 160)
Techniques for organizing data, computing, and interpreting measures of central tendency, variability, and association. Estimation, confidence intervals, tests of hypotheses, t-tests, correlation, and regression. Possible topics: analysis of variance and chi-square tests, computer statistical packages.
Terms: Aut, Win, Spr, Sum
| Units: 5
| UG Reqs: GER:DB-Math, WAY-AQR, WAY-FR
Instructors:
Chi, K. (PI)
;
Fan, J. (PI)
;
Kim, G. (PI)
;
Lim (Chun Hui), C. (PI)
;
Martinez, J. (PI)
;
Schwartz, S. (PI)
;
Walther, G. (PI)
;
Xiang, V. (PI)
;
Abdelrahim, S. (TA)
;
Gunawardana, D. (TA)
;
Jeong, Y. (TA)
;
Ji, W. (TA)
;
Kanekar, R. (TA)
;
Kazdan, J. (TA)
;
Smith, H. (TA)
;
Song, Z. (TA)
;
Tanoh, I. (TA)
;
Yan, R. (TA)
STATS 110: Statistical Methods in Engineering and the Physical Sciences
Introduction to statistics for engineers and physical scientists. Topics: descriptive statistics, probability, interval estimation, tests of hypotheses, nonparametric methods, linear regression, analysis of variance, elementary experimental design. Prerequisite: one year of calculus. Please note that students must enroll in one section in addition to the main lecture.
Terms: Aut
| Units: 5
| UG Reqs: GER:DB-Math, WAY-AQR, WAY-FR
STATS 116: Theory of Probability
Probability spaces as models for phenomena with statistical regularity. Discrete spaces (binomial, hypergeometric, Poisson). Continuous spaces (normal, exponential) and densities. Random variables, expectation, independence, conditional probability. Introduction to the laws of large numbers and central limit theorem. Prerequisites:
MATH 52 and familiarity with infinite series, or equivalent. Undergraduate students enroll for 5 units, graduate students enroll for 4 units. Undergraduate students must enroll in one section in addition to the main lecture. Sections are optional for graduate students. Note: Autumn 2023-24 is the last time this course will be offered. It will be replaced by
STATS 117 and
STATS 118 in 2024-25.
Terms: Aut
| Units: 5
| UG Reqs: GER:DB-Math, WAY-AQR, WAY-FR
STATS 160: Introduction to Statistical Methods: Precalculus (PSYCH 10, STATS 60)
Techniques for organizing data, computing, and interpreting measures of central tendency, variability, and association. Estimation, confidence intervals, tests of hypotheses, t-tests, correlation, and regression. Possible topics: analysis of variance and chi-square tests, computer statistical packages.
Terms: Aut, Win, Spr, Sum
| Units: 5
Instructors:
Chi, K. (PI)
;
Fan, J. (PI)
;
Kim, G. (PI)
;
Lim (Chun Hui), C. (PI)
;
Martinez, J. (PI)
;
Schwartz, S. (PI)
;
Walther, G. (PI)
;
Xiang, V. (PI)
;
Abdelrahim, S. (TA)
;
Gunawardana, D. (TA)
;
Jeong, Y. (TA)
;
Ji, W. (TA)
;
Kanekar, R. (TA)
;
Kazdan, J. (TA)
;
Smith, H. (TA)
;
Song, Z. (TA)
;
Tanoh, I. (TA)
;
Yan, R. (TA)
STATS 199: Independent Study
For undergraduates.
Terms: Aut, Win, Spr, Sum
| Units: 1-15
| Repeatable
for credit
Instructors:
Baiocchi, M. (PI)
;
Duchi, J. (PI)
;
Palacios, J. (PI)
...
more instructors for STATS 199 »
Instructors:
Baiocchi, M. (PI)
;
Duchi, J. (PI)
;
Palacios, J. (PI)
;
Sabatti, C. (PI)
;
Schramm, T. (PI)
;
Taylor, J. (PI)
;
Tibshirani, R. (PI)
;
Wager, S. (PI)
;
Walther, G. (PI)
STATS 200: Introduction to Statistical Inference
Modern statistical concepts and procedures derived from a mathematical framework. Statistical inference, decision theory; point and interval estimation, tests of hypotheses; Neyman-Pearson theory. Bayesian analysis; maximum likelihood, large sample theory. Prerequisite:
STATS 116. Please note that students must enroll in one section in addition to the main lecture.
Terms: Aut, Win, Sum
| Units: 4
Instructors:
Hwang, J. (PI)
;
Johnstone, I. (PI)
;
Palacios, J. (PI)
...
more instructors for STATS 200 »
Instructors:
Hwang, J. (PI)
;
Johnstone, I. (PI)
;
Palacios, J. (PI)
;
Cherian, J. (TA)
;
Jang, J. (TA)
;
Jeong, Y. (TA)
;
Kanekar, R. (TA)
;
Lee, J. (TA)
;
Lu, S. (TA)
;
Ma, G. (TA)
;
Salerno, M. (TA)
;
Tanoh, I. (TA)
;
Xie, R. (TA)
;
Zhang, I. (TA)
;
Zhao, S. (TA)
STATS 202: Data Mining and Analysis
Data mining is used to discover patterns and relationships in data. Emphasis is on large complex data sets such as those in very large databases or through web mining. Topics: decision trees, association rules, clustering, case based methods, and data visualization. Prereqs: Introductory courses in statistics or probability (e.g.,
Stats 60), linear algebra (e.g.,
Math 51), and computer programming (e.g.,
CS 105). May not be taken for credit by students with credit in
STATS 216 or 216V.
Terms: Aut, Sum
| Units: 3
Instructors:
Erdmann-Pham, D. (PI)
;
Tran, L. (PI)
;
Chen, Z. (TA)
...
more instructors for STATS 202 »
Instructors:
Erdmann-Pham, D. (PI)
;
Tran, L. (PI)
;
Chen, Z. (TA)
;
Gonzalez, X. (TA)
;
Hu, A. (TA)
;
Ji, W. (TA)
;
Zhang, J. (TA)
STATS 206: Applied Multivariate Analysis (BIODS 206)
Introduction to the statistical analysis of several quantitative measurements on each observational unit. Emphasis is on concepts, computer-intensive methods. Examples from economics, education, geology, psychology. Topics: multiple regression, multivariate analysis of variance, principal components, factor analysis, canonical correlations, multidimensional scaling, clustering. Pre- or corequisite: 200.
Terms: Aut
| Units: 3
Instructors:
Owen, A. (PI)
;
Li, H. (TA)
STATS 209: Introduction to Causal Inference
This course introduces the fundamental ideas and methods in causal inference, with examples drawn from education, economics, medicine, and digital marketing. Topics include potential outcomes, randomization, observational studies, matching, covariate adjustment, AIPW, heterogeneous treatment effects, instrumental variables, regression discontinuity, and synthetic controls. Prerequisites: basic probability and statistics, familiarity with R.
Terms: Aut
| Units: 3
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