STAT - Statistics

STAT 5 Statistics

Introduction to statistical methods/reasoning, including descriptive methods, data-gathering (experimental design and sample surveys), probability, interval estimation, significance tests, one- and two-sample problems, categorical data analysis, correlation and regression. Emphasis on applications to the natural and social sciences. Students cannot receive credit for this course if they have already received credit for course 7. (Formerly AMS 5.)

Credits

5

Instructor

Herbert Lee, Athanasios Kottas, Bruno Sanso, Bruno Mendes, Jonathan Katznelson, David Draper, Abel Rodriguez

General Education Code

SR

Quarter offered

Fall, Winter, Spring, Summer

STAT 7 Statistical Methods for the Biological, Environmental, and Health Sciences

Case-study-based introduction to statistical methods as practiced in the biological, environmental, and health sciences. Descriptive methods, experimental design, probability, interval estimation, hypothesis testing, one- and two-sample problems, power and sample size calculations, simple correlation and simple linear regression, one-way analysis of variance, categorical data analysis. (Formerly AMS 7.)

Credits

5

Instructor

The Staff, Herbert Lee, Raquel Prado, Bruno Mendes, David Draper, Abel Rodriguez, Ju Hee Lee, Rajarshi Guhaniyogi

Requirements

Prerequisite(s): score of 300 or higher on the mathematics placement examination (MPE), or AM 3 or AM 6 or AM 11A or AM 15A or MATH 3 or MATH 11A or MATH 19A. Concurrent enrollment in STAT 7L is required.

General Education Code

SR

Quarter offered

Fall, Winter, Spring, Summer

STAT 7L Statistical Methods for the Biological, Environmental, and Health Sciences Laboratory

Computer-based laboratory course in which students gain hands-on experience in analysis of data sets arising from statistical problem-solving in the biological, environmental, and health sciences. Descriptive methods, interval estimation, hypothesis testing, one-and two-sample problems, correlation and regression, one-way analysis of variance, categorical data analysis. (Formerly AMS 7L.)

Credits

2

Instructor

The Staff, Herbert Lee, Raquel Prado, David Draper, Abel Rodriguez, Ju Hee Lee, Rajarshi Guhaniyogi

Requirements

Prerequisite(s): score of 300 or higher on the mathematics placement examination (MPE), AM 3 or AM 6 or AM 11A or AM 15A or MATH 3 or MATH 11A or MATH 19A. Concurrent enrollment in STAT 7 is required.

Quarter offered

Fall, Winter, Spring, Summer

STAT 80A Gambling and Gaming

Games of chance and strategy motivated early developments in probability, statistics, and decision theory. Course uses popular games to introduce students to these concepts, which underpin recent scientific developments in economics, genetics, ecology, and physics. (Formerly AMS 80A.)

Credits

5

Instructor

Herbert Lee, Athanasios Kottas, Bruno Mendes, Abel Rodriguez, Rajarshi Guhaniyogi

General Education Code

SR

Quarter offered

Fall, Winter

STAT 80B Data Visualization

Introduces the use of complex-data graphical representations to extract information from data. Topics include: summary statistics, boxplots, histograms, dotplots, scatterplots, bubble plots, and map-creation, as well as visualization of trees and hierarchies, networks and graphs, and text. (Formerly AMS 80B.)

Credits

5

Instructor

The Staff, Abel Rodriguez

General Education Code

SR

Quarter offered

Spring

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

STAT 200 Research and Teaching in Statistics

Focuses on basic teaching techniques for teaching assistants, including responsibilities and rights, leading discussion or lab sessions, presentation techniques, maintaining class records, and grading. Examines research and professional training, including use of library, technical writing, giving seminar and conference talks, and ethical issues in science and engineering.

Credits

3

Instructor

Athanasios Kottas, Ju Hee Lee

Requirements

Enrollment is restricted to graduate students.

Quarter offered

Fall

STAT 202 Linear Models in SAS

Case study-based course teaches statistical linear modeling using the SAS software package. Teaches generalized linear models; linear regression; analysis of variance/covariance; analysis of data with random effects and repeated measures. (Formerly AMS 202.)

Credits

5

Instructor

The Staff, Bruno Mendes

Requirements

Prerequisite(s): STAT 108 or STAT 208, or permission of instructor. Enrollment is restricted to graduate students.

STAT 203 Introduction to Probability Theory

Introduces probability theory and its applications. Requires a multivariate calculus background, but has no measure theoretic content. Topics include: combinatorial analysis; axioms of probability; random variables (discrete and continuous); joint probability distributions; expectation and higher moments; central limit theorem; law of large numbers; and Markov chains. Students cannot receive credit for this course and course 131 or Computer Engineering 107. (Formerly AMS 203.)

Credits

5

Instructor

The Staff, Raquel Prado, Athanasios Kottas, Bruno Sanso, Ju Hee Lee

Requirements

Enrollment is restricted to graduate students, or by permission of the instructor.

Quarter offered

Fall

STAT 204 Introduction to Statistical Data Analysis

Presents tools for exploratory data analysis (EDA) and statistical modeling in R. Topics include numerical and graphical tools for EDA, linear and logistic regression, ANOVA, PCA, and tools for acquiring and storing large data. No R knowledge is required. Enrollment is restricted to graduate students and by permission of instructor. (Formerly AMS 204.)

Credits

5

Instructor

Raquel Prado, Abel Rodriguez

Requirements

Enrollment restricted to graduate students.

Quarter offered

Fall

STAT 205 Introduction to Classical Statistical Learning

Introduction to classical statistical inference. Topic include: random variables and distributions; types of convergence; central limit theorems; maximum likelihood estimation; Newton-Raphson, Fisher scoring, Expectation-Maximization, and stochastic gradient algorithms; confidence intervals; hypothesis testing; ridge regression, lasso, and elastic net.

Credits

5

Instructor

Rajarshi Guhaniyogi

Requirements

Prerequisite(s): STAT 203. Enrollment is restricted to graduate students.

Quarter offered

Winter

STAT 205B Intermediate Classical Inference

Statistical inference from a frequentist point of view. Properties of random samples; convergence concepts applied to point estimators; principles of statistical inference; obtaining and evaluating point estimators with particular attention to maximum likelihood estimates and their properties; obtaining and evaluating interval estimators; and hypothesis testing methods and their properties. (Formerly AMS 205B.)

Credits

5

Instructor

The Staff, Bruno Sanso, David Draper, Rajarshi Guhaniyogi

Requirements

Prerequisite(s): STAT 203 or equivalent. Enrollment is restricted to graduate students.

Quarter offered

Winter

STAT 206 Applied Bayesian Statistics

Introduces Bayesian statistical modeling from a practitioner's perspective. Covers basic concepts (e.g., prior-posterior updating, Bayes factors, conjugacy, hierarchical modeling, shrinkage, etc.), computational tools (Markov chain Monte Carlo, Laplace approximations), and Bayesian inference for some specific models widely used in the literature (linear and generalized linear mixed models). (Formerly AMS 206.)

Credits

5

Instructor

Raquel Prado, Athanasios Kottas, David Draper, Abel Rodriguez

Requirements

Prerequisite(s): STAT 131 or STAT 203, or by permission of the instructor. Enrollment is restricted to graduate students.

Quarter offered

Winter

STAT 206B Intermediate Bayesian Inference

Bayesian statistical methods for inference and prediction including: estimation; model selection and prediction; exchangeability; prior, likelihood, posterior, and predictive distributions; coherence and calibration; conjugate analysis; Markov Chain Monte Carlo methods for simulation-based computation; hierarchical modeling; Bayesian model diagnostics, model selection, and sensitivity analysis. (Formerly AMS 206B.)

Credits

5

Instructor

The Staff, Raquel Prado, Abel Rodriguez, Ju Hee Lee

Requirements

Prerequisite(s): STAT 203. Enrollment is restricted to graduate students; undergraduates may enroll by permission of the instructor.

Quarter offered

Winter

STAT 207 Intermediate Bayesian Statistical Modeling

Hierarchical modeling, linear models (regression and analysis of variance) from the Bayesian point of view, intermediate Markov chain Monte Carlo methods, generalized linear models, multivariate models, mixture models, hidden Markov models. (Formerly AMS 207.)

Credits

5

Instructor

The Staff, Raquel Prado, Bruno Sanso, David Draper

Requirements

Prerequisite(s): STAT 206 or STAT 206B; enrollment is restricted to graduate students or by permission of instructor.

Quarter offered

Spring

STAT 208 Linear Statistical Models

Theory, methods, and applications of linear statistical models. Review of simple correlation and simple linear regression. Multiple and partial correlation and multiple linear regression. Analysis of variance and covariance. Linear model diagnostics and model selection. Case studies drawn from natural, social, and medical sciences. Course 205 strongly recommended as a prerequisite. Undergraduates are encouraged to take this class with permission of instructor. (Formerly AMS 256.)

Credits

5

Instructor

The Staff, Raquel Prado, Bruno Sanso, Abel Rodriguez, Ju Hee Lee, Rajarshi Guhaniyogi

Requirements

Prerequisite(s): course STAT 205B or permission of instructor. Enrollment is restricted to graduate students.

Quarter offered

Spring

STAT 209 Generalized Linear Models

Theory, methods, and applications of generalized linear statistical models; review of linear models; binomial models for binary responses (including logistical regression and probit models); log-linear models for categorical data analysis; and Poisson models for count data. Case studies drawn from social, engineering, and life sciences. (Formerly AMS 274.)

Credits

5

Instructor

The Staff, Athanasios Kottas

Requirements

Prerequisite(s): STAT 205B or STAT 256. Enrollment is restricted to graduate students.

Quarter offered

Fall

STAT 222 Bayesian Nonparametric Methods

Theory, methods, and applications of Bayesian nonparametric modeling. Prior probability models for spaces of functions. Dirichlet processes. Polya trees. Nonparametric mixtures. Models for regression, survival analysis, categorical data analysis, and spatial statistics. Examples drawn from social, engineering, and life sciences. (Formerly AMS 241.)

Credits

5

Instructor

Athanasios Kottas, Abel Rodriguez

Requirements

Prerequisite(s): STAT 207. Enrollment is restricted to graduate students.

Quarter offered

Spring

STAT 223 Time Series Analysis

Graduate level introductory course on time series data and models in the time and frequency domains: descriptive time series methods; the periodogram; basic theory of stationary processes; linear filters; spectral analysis; time series analysis for repeated measurements; ARIMA models; introduction to Bayesian spectral analysis; Bayesian learning, forecasting, and smoothing; introduction to Bayesian Dynamic Linear Models (DLMs); DLM mathematical structure; DLMs for trends and seasonal patterns; and autoregression and time series regression models. (Formerly AMS 223.)

Credits

5

Instructor

Raquel Prado

Requirements

Prerequisite(s): STAT 206B, or by permission of instructor. Enrollment is restricted to graduate students.

Quarter offered

Winter

STAT 224 Bayesian Survival Analysis and Clinical Design

Introduction to Bayesian statistical methods for survival analysis and clinical trial design: parametric and semiparametric models for survival data, frailty models, cure rate models, the design of clinical studies in phase I/II/III. (Formerly AMS 276.)

Credits

5

Instructor

Ju Hee Lee

Requirements

Prerequisite(s): STAT 207 or by permission of instructor. Enrollment is restricted to graduate students.

Quarter offered

Fall

STAT 225 Multivariate Statistical Methods

Introduction to statistical methods for analyzing data sets in which two or more variables play the role of outcome or response. Descriptive methods for multivariate data. Matrix algebra and random vectors. The multivariate normal distribution. Likelihood and Bayesian inferences about multivariate mean vectors. Analysis of covariance structure: principle components, factor analysis. Discriminant, classification and cluster analysis. (Formerly AMS 225.)

Credits

5

Instructor

The Staff, David Draper, Ju Hee Lee

Requirements

Prerequisite(s): STAT 206 or STAT 206B, or by permission of instructor. Enrollment is restricted to graduate students.

STAT 226 Spatial Statistics

Introduction to the analysis of spatial data: theory of correlation structures and variograms; kriging and Gaussian processes; Markov random fields; fitting models to data; computational techniques; frequentist and Bayesian approaches. (Formerly AMS 245.)

Credits

5

Instructor

Herbert Lee, Bruno Sanso

Requirements

Prerequisite(s): STAT 207. Enrollment is restricted to graduate students.

STAT 229 Advanced Bayesian Computation

Teaches some advanced techniques in Bayesian Computation. Topics include Hamiltonian Monte Carlo; slice sampling; sequential Monte Carlo; assumed density filtering; expectation propagation; stochastic gradient descent; approximate Markov chain Monte Carlo; variational inference; and stochastic variational inference. (Formerly AMS 268.)

Credits

5

Instructor

Rajarshi Guhaniyogi

Requirements

Prerequisite(s): STAT 207, or by permission of the instructor. Enrollment is restricted to graduate students; undergraduates may enroll by permission of the instructor.

STAT 243 Stochastic Processes

Includes probabilistic and statistical analysis of random processes, continuous-time Markov chains, hidden Markov models, point processes, Markov random fields, spatial and spatio-temporal processes, and statistical modeling and inference in stochastic processes. Applications to a variety of fields. (Formerly AMS 263.)

Credits

5

Instructor

The Staff, Athanasios Kottas, Abel Rodriguez, Rajarshi Guhaniyogi

Requirements

Prerequisite(s): STAT 205B or STAT 246, or by permission of instructor.

STAT 244 Bayesian Decision Theory

Explores conceptual and theoretical bases of statistical decision making under uncertainty. Focuses on axiomatic foundations of expected utility, elicitation of subjective probabilities and utilities, and the value of information and modern computational methods for decision problems. (Formerly AMS 221.)

Credits

5

Instructor

Bruno Sanso, David Draper

Requirements

Prerequisite(s): STAT 206. Enrollment is restricted to graduate students.

STAT 246 Probability Theory with Markov Chains

Introduction to probability theory: probability spaces, expectation as Lebesgue integral, characteristic functions, modes of convergence, conditional probability and expectation, discrete-state Markov chains, stationary distributions, limit theorems, ergodic theorem, continuous-state Markov chains, applications to Markov chain Monte Carlo methods. (Formerly AMS 261.)

Credits

5

Instructor

Athanasios Kottas

Requirements

Prerequisite(s): STAT 205B or by permission of instructor. Enrollment is restricted to graduatestudents.

STAT 266A Data Visualization and Statistical Programming in R

Introduces students to data visualization and statistical programming techniques using the R language. Covers the basics of the language, descriptive statistics, visual analytics, and applied linear regression. Enrollment is by permission of the instructor. (Formerly Applied Math and Statistics 266A and Computer Science 266A.)

Credits

3

Cross Listed Courses

CSE 266A

Instructor

The Staff, Suresh Lodha, Abel Rodriguez

Quarter offered

Fall

STAT 266B Advanced Statistical Programming in R

Teaches students already familiar with the R language advanced tools such as interactive graphics, interfacing with low-level languages, package construction, debugging, profiling, and parallel computation. (Formerly Applied Math and Statistics 266B and Computer Science 266B.)

Credits

3

Cross Listed Courses

CSE 266B

Instructor

Suresh Lodha, Abel Rodriguez

Requirements

Prerequisite(s): STAT 266A or CSE 266A.

STAT 266C Introduction to Data Wrangling

Introduces students to concepts and tools associated with data collection, curation, manipulation, and cleaning including an introduction to relational databases and SQL, regular expressions, API usage, and web scraping using Python. (Formerly Applied Math and Statistics 266C and Computer Science 266C.)

Credits

3

Cross Listed Courses

CSE 266C

Instructor

Suresh Lodha, Abel Rodriguez

Requirements

Prerequisite(s): STAT 266A or CSE 266A.

STAT 280B Seminars in Statistics

Weekly seminar series covering topics of current research in statistics.

Credits

2

Instructor

Ju Hee Lee

Requirements

Enrollment is restricted to graduate students.

Repeatable for credit

Yes

Quarter offered

Fall

STAT 280D Seminar in Bayesian Statistical Methodology

Weekly seminar/discussion group on Bayesian statistical methods, covering both analytical and computational approaches. Participants present research progress and finding in semiformal discussions. Students must present their own research on a regular basis. (Formerly AMS 280D.)

Credits

2

Requirements

Enrollment is restricted to graduate students.

Repeatable for credit

Yes

STAT 285 Seminar in Career Skills

Seminar in career skills for applied mathematicians and statisticians. Learn about professional activities such as the publication process, grant proposals, and the job market.

Credits

2

Instructor

Herbert Lee

Requirements

Enrollment is restricted to graduate students, typically within two years of their expected Ph.D. completion date.

STAT 291 Advanced Topics in Bayesian Statistics

Advanced study of research topics in the theory, methods, or applications of Bayesian statistics. The specific subject depends on the instructor. Enrollment is restricted to graduate students and by permission of instructor. (Formerly AMS 291.)

Credits

3

Repeatable for credit

Yes

STAT 296 Masters Project

Independent completion of a masters project under faculty supervision. Students submit petition to sponsoring agency. Enrollment is restricted to graduate students.

Credits

2

STAT 297A Independent Study or Research

Independent study or research under faculty supervision. Students submit petition to sponsoring agency. Enrollment is restricted to graduate students.

Credits

5

Quarter offered

Fall, Winter, Spring

STAT 297B Independent Study or Research

Independent study or research under faculty supervision. Students submit petition to sponsoring agency. Enrollment is restricted to graduate students.

Credits

10

STAT 297C Independent Study or Research

Independent study or research under faculty supervision. Students submit petition to sponsoring agency. Enrollment is restricted to graduate students.

Credits

15

STAT 297F Independent Study

Independent study or research under faculty supervision. Students submit petition to sponsoring agency. Enrollment is restricted to graduate students.

Credits

2

STAT 299A Thesis Research

Thesis research under faculty supervision. Students submit petition to sponsoring agency. Enrollment restricted to graduate students.

Credits

5

STAT 299B Thesis Research

Thesis research under faculty supervision. Students submit petition to sponsoring agency. Enrollment restricted to graduate students.

Credits

10

STAT 299C Thesis Research

Thesis research under faculty supervision. Students submit petition to sponsoring agency. Enrollment restricted to graduate students.

Credits

15