printer friendly pageSTATS 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:
Green, A. (PI)
;
Martinez, J. (PI)
;
Poldrack, R. (PI)
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Instructors:
Green, A. (PI)
;
Martinez, J. (PI)
;
Poldrack, R. (PI)
;
Tanoh, I. (PI)
;
Walther, G. (PI)
;
Ji, W. (TA)
;
Kazdan, J. (TA)
;
Liang, L. (TA)
;
Lu, S. (TA)
;
Martinez, J. (TA)
;
Mortazavi, L. (TA)
;
Rajanala, S. (TA)
;
Rose, D. (TA)
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Schwartz, S. (TA)
;
Seiler, B. (TA)
;
Song, Z. (TA)
;
Xu, A. (TA)
STATS 100: Mathematics of Sports
This course will teach you how statistics and probability can be applied in sports, in order to evaluate team and individual performance, build optimal in-game strategies and ensure fairness between participants. Topics will include examples drawn from multiple sports such as basketball, baseball, soccer, football and tennis. The course is intended to focus on data-based applications, and will involve computations in R with real data sets via tutorial sessions and homework assignments. Prereqs: No statistical or programming background is assumed, but introductory courses, e.g,
Stats 60,101 or 116, are recommended. A prior knowledge of Linear Algebra (e.g.,
Math 51) and basic probability is strongly recommended.
Terms: Spr
| Units: 3
| UG Reqs: WAY-AQR, GER:DB-Math
STATS 101: Data Science 101
This course will provide a hands-on introduction to statistics and data science. Students will engage with fundamental ideas in inferential and computational thinking. Each week consists of three lectures and two labs, in which students will manipulate real-world data and learn about statistical and computational tools. Topics covered include introductions to data visualization techniques, summary statistics, regression, prediction, sampling variability, statistical testing, inference, and replicability. The objectives of this course are to have students (1) be able to connect data to underlying phenomena and think critically about conclusions drawn from data analysis, and (2) be knowledgeable about how to carry out their own data analysis later. Some statistical background or programming experience is helpful, but not required. The class will start with a brief introduction to R but will move at a relatively fast pace. Freshmen and sophomores interested in data science, computing, and statistics are encouraged to attend. Also open to graduate students.
Terms: Spr, Sum
| Units: 5
| UG Reqs: GER: DB-NatSci, WAY-AQR
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.
Terms: Aut, Spr, Sum
| Units: 5
| UG Reqs: GER:DB-Math, WAY-AQR, WAY-FR
Instructors:
Li, H. (PI)
;
Li, S. (PI)
;
Schramm, T. (PI)
;
Hartog, W. (TA)
;
Howes, M. (TA)
;
Jang, J. (TA)
;
Kanekar, R. (TA)
;
Li, H. (TA)
;
Tung, N. (TA)
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Xie, R. (TA)
;
Zhang, J. (TA)
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
STATS 199: Independent Study
For undergraduates.
Terms: Aut, Win, Spr, Sum
| Units: 1-15
| Repeatable
for credit
Instructors:
Baiocchi, M. (PI)
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Duchi, J. (PI)
;
Lai, T. (PI)
;
Palacios, J. (PI)
;
Rogosa, D. (PI)
;
Sabatti, C. (PI)
;
Schramm, T. (PI)
;
Taylor, J. (PI)
;
Tibshirani, R. (PI)
;
Wager, S. (PI)
;
Walther, G. (PI)
STATS 204: Sampling
How best to take data and where to sample it. Examples include surveys and sampling from data warehouses. Emphasis is on methods for finite populations. Topics: simple random sampling, stratified sampling, cluster sampling, ratio and regression estimators, two stage sampling.
Terms: Spr
| Units: 3
Instructors:
Owen, A. (PI)
STATS 205: Introduction to Nonparametric Statistics
Nonparametric regression and nonparametric density estimation, modern nonparametric techniques, nonparametric confidence interval estimates, nearest neighbor algorithms (with non-linear features), wavelet, bootstrap. Nonparametric analogs of the one- and two-sample t-tests and analysis of variance
Terms: Spr
| Units: 3
Instructors:
Ma, T. (PI)
;
Pan, Z. (TA)
STATS 206: Applied Multivariate Analysis
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: Spr
| Units: 3
Instructors:
Hastie, T. (PI)
;
Ghandwani, D. (TA)
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