Statistics (STAT)
STAT 0PX Preparatory Statistics 1 Unit
Department: Statistics
Course level: Undergraduate
Term course may be offered: Summer
Grading: Offered for pass/not pass grade only.
Hours and format: 5 hours of Lecture and 4.5 hours of Workshop per week for 8 weeks. 5 hours of Lecture and 4.5 hours of Workshop per week for 6 weeks.
Prerequisites: Consent of instructor.
This course assists entering Freshman students with basic statistical concepts and problem solving. Designed for students who do not meet the prerequisites for 2. Offered through the Student Learning Center.
Instructor: Purves
STAT 2 Introduction to Statistics 4 Units
Department: Statistics
Course level: Undergraduate
Terms course may be offered: Fall, spring and summer
Grading: Letter grade.
Hours and format: 3 hours of Lecture and 2 hours of Laboratory per week for 15 weeks. 5 hours of Lecture and 4 hours of Laboratory per week for 8 weeks.
Population and variables. Standard measures of location, spread and association. Normal approximation. Regression. Probability and sampling. Binomial distribution. Interval estimation. Some standard significance tests.
Students who have taken 2X, 5, 20, 21, 21X, or 25 will receive no credit for 2.
STAT 20 Introduction to Probability and Statistics 4 Units
Department: Statistics
Course level: Undergraduate
Terms course may be offered: Fall, spring and summer
Grading: Letter grade.
Hours and format: 3 hours of Lecture and 2 hours of Laboratory per week for 15 weeks. 6 hours of Lecture and 3 hours of Laboratory per week for 8 weeks.
Prerequisites: One semester of calculus.
For students with mathematical background who wish to acquire basic concepts. Relative frequencies, discrete probability, random variables, expectation. Testing hypotheses. Estimation. Illustrations from various fields.
Students who have taken 2, 2X, 5, 21, 21X, or 25 will receive no credit for 20.
STAT 21 Introductory Probability and Statistics for Business 4 Units
Department: Statistics
Course level: Undergraduate
Terms course may be offered: Fall, spring and summer
Grading: Letter grade.
Hours and format: 3 hours of Lecture and 2 hours of Laboratory per week for 15 weeks. 5 hours of Lecture and 4 hours of Laboratory per week for 8 weeks.
Prerequisites: One semester of calculus.
Descriptive statistics, probability models and related concepts, sample surveys, estimates, confidence intervals, tests of significance, controlled experiments vs. observational studies, correlation and regression.
Students who have taken 2, 2X, 5, 20, 21X or 25 will receive no credit for 21. A deficency in N21 may be moved by taking 21.
STAT W21 Introductory Probability and Statistics for Business 4 Units
Department: Statistics
Course level: Undergraduate
Terms course may be offered: Fall, spring and summer
Grading: Letter grade.
Hours and format: 3 hours of Web-based lecture per week for 15 weeks. 7.5 hours of Web-based lecture per week for 8 weeks. This is an online course.
Prerequisites: One semester of calculus.
Reasoning and fallacies, descriptive statistics, probability models and related concepts, combinatorics, sample surveys, estimates, confidence intervals, tests of significance, controlled experiments vs. observational studies, correlation and regression.
Students will receive no credit for Statistics W21 after taking Statistics 2, 20, or 25. Formerly known as N21.
STAT 39D Freshman/Sophomore Seminar 2 - 4 Units
Department: Statistics
Course level: Undergraduate
Terms course may be offered: Fall and spring
Grading: The grading option will be decided by the instructor when the class is offered.
Hours and format: Seminar format.
Prerequisites: Priority given to freshmen and sophomores.
Freshman and sophomore seminars offer lower division students the opportunity to explore an intellectual topic with a faculty member and a group of peers in a small-seminar setting. These seminars are offered in all campus departments; topics vary from department to department and from semester to semester.
Course may be repeated for credit when topic changes.
STAT C79/COMPSCI C79/POL SCI C79 Societal Risks and the Law 3 Units
Department: Statistics; Computer Science; Political Science
Course level: Undergraduate
Terms course may be offered: Fall and spring
Grading: Letter grade.
Hours and format: 3 hours of Lecture and 1 hour of Discussion per week for 15 weeks.
Defining, perceiving, quantifying and measuring risk; identifying risks and estimating their importance; determining whether laws and regulations can protect us from these risks; examining how well existing laws work and how they could be improved; evaluting costs and benefits. Applications may vary by term. This course cannot be used to complete engineering unit or technical elective requirements for students in the College of Engineering.
STAT 97 Field Study in Statistics 1 - 3 Units
Department: Statistics
Course level: Undergraduate
Terms course may be offered: Fall, spring and summer
Grading: Offered for pass/not pass grade only.
Hours and format: 1 to 3 hour of Fieldwork per week for 15 weeks. 1.5 to 5.5 hours of Fieldwork per week for 8 weeks. 2.5 to 7.5 hours of Fieldwork per week for 6 weeks.
Supervised experience relevant to specific aspects of statistics in off-campus settings. Individual and/or group meetings with faculty.
Course may be repeated for credit. Course may be repeated for credit when topic changes.
STAT 98 Directed Group Study 2 Units
Department: Statistics
Course level: Undergraduate
Terms course may be offered: Fall, spring and summer
Grading: Offered for pass/not pass grade only.
Hours and format: 2 hours of group study per week.
Prerequisites: Consent of instructor.
Must be taken at the same time as either Statistics 2 or 21. This course assists lower division statistics students with structured problem solving, interpretation and making conclusions.
Course may be repeated for credit when topic changes.
STAT 100 Introduction to the SAS System for Data Analysis 1 Unit
Department: Statistics
Course level: Undergraduate
Term course may be offered: Summer
Grading: Offered for pass/not pass grade only.
Hours and format: 5 hours of Lecture per week for 3 weeks.
The SAS system is useful for reading input data from a variety of sources and then performing a wide range of analyses and graphical displays with the data. Topics include accessing SAS on a variety of computer platforms; inputting raw data; managing SAS data sets; programming in SAS and in the SAS macro language. Emphasis on large data sets. Students are encouraged to bring in their own data. Students should have used at least one program, such as a word processor.
Instructor: Spector
STAT 131A Introduction to Probability and Statistics for Life Scientists 4 Units
Department: Statistics
Course level: Undergraduate
Terms course may be offered: Fall, spring and summer
Grading: Letter grade.
Hours and format: 3 hours of Lecture and 2 hours of Laboratory per week for 15 weeks. 5 hours of Lecture and 4 hours of Laboratory per week for 8 weeks.
Prerequisites: One semester of calculus or consent of instructor.
Ideas for estimation and hypothesis testing basic to applications, including an introduction to probability. Linear estimation and normal regression theory.
STAT 132 Practical Machine Learning 3 Units
Department: Statistics
Course level: Undergraduate
Term course may be offered: Summer
Grading: Letter grade.
Hours and format: 4 hours of Lecture per week for 8 weeks.
Prerequisites: Some prior exposure to basic probability and to linear algebra.
Machine learning is a collection of topics in which the focus is on large-scale statistical problems where computational issues are paramount. The goal is often one of prediction or classification, where based on a set of labeled data it is desired to predict the lablels of unlabeled data. Machine learning algorithms also often focus on exploratory data analysis. This course will introduce core statistical machine learning algorithms in a non-mathematical way, emphasizing applied problem-solving.
STAT 133 Concepts in Computing with Data 3 Units
Department: Statistics
Course level: Undergraduate
Terms course may be offered: Fall, spring and summer
Grading: Letter grade.
Hours and format: 3 hours of lecture and 1 hour of computer laboratory per week. 3.5 hours of lecture and 3.5 hours of computer laboratory per week for 8 weeks. 4 hours of lecture and 2 hours of computer laboratory per week for 10 weeks.
An introduction to computationally intensive applied statistics. Topics will include organization and use of databases, visualization and graphics, statistical learning and data mining, model validation procedures, and the presentation of results.
STAT 134 Concepts of Probability 3 Units
Department: Statistics
Course level: Undergraduate
Terms course may be offered: Fall, spring and summer
Grading: Letter grade.
Hours and format: 3 hours of Lecture per week for 15 weeks. 5 hours of Lecture per week for 8 weeks.
Prerequisites: One year of calculus.
An introduction to probability, emphasizing concepts and applications. Conditional expectation, independence, laws of large numbers. Discrete and continuous random variables. Central limit theorem. Selected topics such as the Poisson process, Markov chains, characteristic functions.
Students will not receive credit for 134 after taking 101.
STAT 135 Concepts of Statistics 4 Units
Department: Statistics
Course level: Undergraduate
Terms course may be offered: Fall, spring and summer
Grading: Letter grade.
Hours and format: 3 hours of Lecture and 2 hours of Laboratory per week for 15 weeks. 6 hours of Lecture and 4 hours of Laboratory per week for 8 weeks.
Prerequisites: Statistics 134 and linear algebra (Mathematics 54 or equivalent). Statistics 133 strongly recommended.
A comprehensive survey course in statistical theory and methodology. Topics include descriptive statistics, maximum likelihood estimation, non-parametric methods, introduction to optimality, goodness-of-fit tests, analysis of variance, bootstrap and computer-intensive methods and least squares estimation. The laboratory includes computer-based data-analytic applications to science and engineering.
STAT 150 Stochastic Processes 3 Units
Department: Statistics
Course level: Undergraduate
Term course may be offered: Spring
Grading: Letter grade.
Hours and format: 3 hours of Lecture per week for 15 weeks.
Prerequisites: 101 or 103A or 134.
Random walks, discrete time Markov chains, Poisson processes. Further topics such as: continuous time Markov chains, queueing theory, point processes, branching processes, renewal theory, stationary processes, Gaussian processes.
STAT 151A Linear Modelling: Theory and Applications 4 Units
Department: Statistics
Course level: Undergraduate
Terms course may be offered: Fall and spring
Grading: Letter grade.
Hours and format: 3 hours of Lecture and 2 hours of Laboratory per week for 15 weeks.
Prerequisites: 102 or 135. 133 recommended.
A coordinated treatment of linear and generalized linear models and their application. Linear regression, analysis of variance and covariance, random effects, design and analysis of experiments, quality improvement, log-linear models for discrete multivariate data, model selection, robustness, graphical techniques, productive use of computers, in-depth case studies.
STAT 151B Linear Modelling: Theory and Applications 4 Units
Department: Statistics
Course level: Undergraduate
Terms course may be offered: Fall and spring
Grading: Letter grade.
Hours and format: 3 hours of Lecture and 2 hours of Laboratory per week for 15 weeks.
Prerequisites: 102 or 135. 133 recommended.
A coordinated treatment of linear and generalized linear models and their application. Linear regression, analysis of variance and covariance, random effects, design and analysis of experiments, quality improvement, log-linear models for discrete multivariate data, model selection, robustness, graphical techniques, productive use of computers, in-depth case studies.
STAT 152 Sampling Surveys 4 Units
Department: Statistics
Course level: Undergraduate
Terms course may be offered: Fall and spring
Grading: Letter grade.
Hours and format: 3 hours of Lecture and 2 hours of Laboratory per week for 15 weeks.
Prerequisites: 101 or 134. 133 and 135 recommended.
Theory and practice of sampling from finite populations. Simple random, stratified, cluster, and double sampling. Sampling with unequal probabilities. Properties of various estimators including ratio, regression, and difference estimators. Error estimation for complex samples.
STAT 153 Introduction to Time Series 4 Units
Department: Statistics
Course level: Undergraduate
Terms course may be offered: Fall and spring
Grading: Letter grade.
Hours and format: 3 hours of Lecture and 2 hours of Laboratory per week for 15 weeks.
Prerequisites: 101, 134 or consent of instructor. 133 or 135 recommended.
An introduction to time series analysis in the time domain and spectral domain. Topics will include: estimation of trends and seasonal effects, autoregressive moving average models, forecasting, indicators, harmonic analysis, spectra.
STAT 154 Modern Statistical Prediction and Machine Learning 4 Units
Department: Statistics
Course level: Undergraduate
Terms course may be offered: Fall and spring
Grading: Letter grade.
Hours and format: 3 hours of Lecture and 2 hours of Laboratory per week for 15 weeks.
Prerequisites: Mathematics 53 and 54 or equivalents; Statistics 135 or equivalent; experience with some programming language. Mathematics 55 or equivalent exposure to counting arguments is recommended but not required.
Theory and practice of statistical prediction. Contemporary methods as extensions of classical methods. Topics: optimal prediction rules, the curse of dimensionality, empirical risk, linear regression and classification, basis expansions, regularization, splines, the bootstrap, model selection, classification and regression trees, boosting, support vector machines. Computational efficiency versus predictive performance. Emphasis on experience with real data and assessing statistical assumptions.
STAT 155 Game Theory 3 Units
Department: Statistics
Course level: Undergraduate
Terms course may be offered: Fall, spring and summer
Grading: Letter grade.
Hours and format: 3 hours of Lecture per week for 15 weeks. 6 hours of Lecture per week for 8 weeks.
Prerequisites: 101 or 134.
General theory of zero-sum, two-person games, including games in extensive form and continuous games, and illustrated by detailed study of examples.
STAT 157 Seminar on Topics in Probability and Statistics 3 Units
Department: Statistics
Course level: Undergraduate
Terms course may be offered: Fall and spring
Grading: Letter grade.
Hours and format: 3 hours of Seminar per week for 15 weeks.
Prerequisites: Mathematics 53-54, Statistics 134, 135. Knowledge of scientific computing environment (R or Matlab) often required. Prerequisites might vary with instructor and topics.
Substantial student participation required. The topics to be covered each semester that the course may be offered will be announced by the middle of the preceding semester; see departmental bulletins. Recent topics include: Bayesian statistics, statistics and finance, random matrix theory, high-dimensional statistics.
Course may be repeated for credit with consent of instructor. Course may be repeated for credit when topic changes.
STAT 158 The Design and Analysis of Experiments 4 Units
Department: Statistics
Course level: Undergraduate
Terms course may be offered: Fall and spring
Grading: Letter grade.
Hours and format: 3 hours of Lecture and 2 hours of Laboratory per week for 15 weeks.
Prerequisites: Statistics 134 and 135 or consent of instructor. Statistics 135 may be taken concurrently. Statistics 133 is recommended.
An introduction to the design and analysis of experiments. This course covers planning, conducting, and analyzing statistically designed experiments with an emphasis on hands-on experience. Standard designs studied include factorial designs, block designs, latin square designs, and repeated measures designs. Other topics covered include the principles of design, randomization, ANOVA, response surface methodoloy, and computer experiments.
STAT H195 Special Study for Honors Candidates 1 - 4 Units
Department: Statistics
Course level: Undergraduate
Terms course may be offered: Fall, spring and summer
Grading: Letter grade.
Hours and format: Zero hours of Independent study per week for 15 weeks. 1 to 4 hour of Independent study per week for 8 weeks. 1 to 5 hour of Independent study per week for 6 weeks.
Course may be repeated for credit. Course may be repeated for credit when topic changes.
STAT 197 Field Study in Statistics 1 - 3 Units
Department: Statistics
Course level: Undergraduate
Terms course may be offered: Fall, spring and summer
Grading: Offered for pass/not pass grade only.
Hours and format: 1 to 3 hour of Fieldwork per week for 15 weeks. 1.5 to 4.5 hours of Fieldwork per week for 10 weeks. 2 to 6 hours of Fieldwork per week for 8 weeks.
Supervised experience relevant to specific aspects of statistics in off-campus settings. Individual and/or group meetings with faculty.
Course may be repeated for credit. Course may be repeated for credit when topic changes. Enrollment is restricted; see the Introduction to Courses and Curricula section of this catalog.
STAT 198 Directed Study for Undergraduates 1 - 3 Units
Department: Statistics
Course level: Undergraduate
Terms course may be offered: Fall, spring and summer
Grading: Offered for pass/not pass grade only.
Hours and format: 1 to 3 hour of Directed group study per week for 15 weeks. 1.5 to 5.5 hours of Directed group study per week for 8 weeks. 2.5 to 7.5 hours of Directed group study per week for 6 weeks.
Prerequisites: Consent of instructor.
Special tutorial or seminar on selected topics.
Course may be repeated for credit. Course may be repeated for credit when topic changes.
STAT 199 Supervised Independent Study and Research 1 - 3 Units
Department: Statistics
Course level: Undergraduate
Terms course may be offered: Fall, spring and summer
Grading: Offered for pass/not pass grade only.
Hours and format: Zero hours of Independent study per week for 15 weeks. 1 to 3 hour of Independent study per week for 8 weeks. 1 to 4 hour of Independent study per week for 6 weeks.
Course may be repeated for credit. Course may be repeated for credit when topic changes.
STAT 200A Introduction to Probability and Statistics at an Advanced Level 4 Units
Department: Statistics
Course level: Graduate
Terms course may be offered: Fall and spring
Grading: Letter grade.
Hours and format: 3 hours of Lecture and 2 hours of Laboratory per week for 15 weeks.
Prerequisites: Multivariable calculus and one semester of linear algebra.
Probability spaces, random variables, distributions in probability and statistics, central limit theorem, Poisson processes, transformations involving random variables, estimation, confidence intervals, hypothesis testing, linear models, large sample theory, categorical models, decision theory.
Students will receive no credit for Statistics 200A-200B after taking Statistics 201A-201B.
STAT 200B Introduction to Probability and Statistics at an Advanced Level 4 Units
Department: Statistics
Course level: Graduate
Terms course may be offered: Fall and spring
Grading: Letter grade.
Hours and format: 3 hours of Lecture and 2 hours of Laboratory per week for 15 weeks.
Prerequisites: Multivariable calculus and one semester of linear algebra.
Probability spaces, random variables, distributions in probability and statistics, central limit theorem, Poisson processes, transformations involving random variables, estimation, confidence intervals, hypothesis testing, linear models, large sample theory, categorical models, decision theory.
Students will receive no credit for Statistics 200A-200B after taking Statistics 201A-201B.
STAT 201A Introduction to Probability at an Advanced Level 4 Units
Department: Statistics
Course level: Graduate
Term course may be offered: Fall
Grading: Letter grade.
Hours and format: 6 hours of Lecture and 3 hours of Laboratory per week for 7 weeks.
Prerequisites: Multivariable calculus, one semester of linear algebra, and Statistics 134 or consent of instructor.
Distributions in probability and statistics, central limit theorem, Poisson processes, modes of convergence, transformations involving random variables.
Students will receive no credit for 201A after taking 200A.
STAT 201B Introduction to Statistics at an Advanced Level 4 Units
Department: Statistics
Course level: Graduate
Term course may be offered: Fall
Grading: Letter grade.
Hours and format: 6 hours of Lecture and 3 hours of Laboratory per week for 7 weeks.
Prerequisites: Statistics 200A, Statistics 201A, or consent of instructor.
Estimation, confidence intervals, hypothesis testing, linear models, large sample theory, categorical models, decision theory.
Students will receive no credit for 201B after taking 200B.
STAT 204 Probability for Applications 4 Units
Department: Statistics
Course level: Graduate
Term course may be offered: Fall
Grading: Letter grade.
Hours and format: 3 hours of Lecture per week for 15 weeks.
A treatment of ideas and techniques most commonly found in the applications of probability: Gaussian and Poisson processes, limit theorems, large deviation principles, information, Markov chains and Markov chain Monte Carlo, martingales, Brownian motion and diffusion.
Students will receive no credit for 204 after taking 205A-205B. Instructor: Evans
STAT C205A/MATH C218A Probability Theory 4 Units
Department: Statistics; Mathematics
Course level: Graduate
Terms course may be offered: Fall and spring
Grading: Letter grade.
Hours and format: 3 hours of Lecture per week for 15 weeks.
The course is designed as a sequence with Statistics C205B/Mathematics C218B with the following combined syllabus. Measure theory concepts needed for probability. Expection, distributions. Laws of large numbers and central limit theorems for independent random variables. Characteristic function methods. Conditional expectations, martingales and martingale convergence theorems. Markov chains. Stationary processes. Brownian motion.
STAT C205B/MATH C218B Probability Theory 4 Units
Department: Statistics; Mathematics
Course level: Graduate
Terms course may be offered: Fall and spring
Grading: Letter grade.
Hours and format: 3 hours of Lecture per week for 15 weeks.
The course is designed as a sequence with with Statistics C205A/Mathematics C218A with the following combined syllabus. Measure theory concepts needed for probability. Expection, distributions. Laws of large numbers and central limit theorems for independent random variables. Characteristic function methods. Conditional expectations, martingales and martingale convergence theorems. Markov chains. Stationary processes. Brownian motion.
STAT C206A/MATH C223A Advanced Topics in Probability and Stochastic Process 3 Units
Department: Statistics; Mathematics
Course level: Graduate
Terms course may be offered: Fall and spring
Grading: Letter grade.
Hours and format: 3 hours of Lecture per week for 15 weeks.
Prerequisites: Statistics C205A-C205B or consent of instructor.
The topics of this course change each semester, and multiple sections may be offered. Advanced topics in probability offered according to students demand and faculty availability.
Course may be repeated for credit with a different instructor. Course may be repeated for credit when topic changes.
STAT C206B/MATH C223B Advanced Topics in Probability and Stochastic Processes 3 Units
Department: Statistics; Mathematics
Course level: Graduate
Terms course may be offered: Fall and spring
Grading: Letter grade.
Hours and format: 3 hours of Lecture per week for 15 weeks.
The topics of this course change each semester, and multiple sections may be offered. Advanced topics in probability offered according to students demand and faculty availability.
Course may be repeated for credit with a different instructor. Course may be repeated for credit when topic changes.
STAT 210A Theoretical Statistics 4 Units
Department: Statistics
Course level: Graduate
Terms course may be offered: Fall and spring
Grading: Letter grade.
Hours and format: 3 hours of Lecture per week for 15 weeks.
Prerequisites: Linear algebra, real analysis, and a year of upper division probability and statistics.
An introduction to mathematical statistics, covering both frequentist and Bayesian aspects of modeling, inference, and decision-making. Topics include statistical decision theory; point estimation; minimax and admissibility; Bayesian methods; exponential families; hypothesis testing; confidence intervals; small and large sample theory; and M-estimation.
STAT 210B Theoretical Statistics 4 Units
Department: Statistics
Course level: Graduate
Term course may be offered: Fall
Grading: Letter grade.
Hours and format: 3 hours of Lecture per week for 15 weeks.
Prerequisites: Statistics 210A and a graduate level probability course; a good understanding of various notions of stochastic convergence.
Introduction to modern theory of statistics; empirical processes, influence functions, M-estimation, U and V statistics and associated stochastic decompositions; non-parametric function estimation and associated minimax theory; semiparametric models; Monte Carlo methods and bootstrap methods; distributionfree and equivariant procedures; topics in machine learning. Topics covered may vary with instructor.
STAT 212A Topics in Theoretical Statistics 3 Units
Department: Statistics
Course level: Graduate
Terms course may be offered: Fall and spring
Grading: Letter grade.
Hours and format: 3 hours of Lecture per week for 15 weeks.
Prerequisites: 210 or 205 and 215.
This course introduces the student to topics of current research interest in theoretical statistics. Recent topics include information theory, multivariate analysis and random matrix theory, high-dimensional inference. Typical topics have been model selection; empirical and point processes; the bootstrap, stochastic search, and Monte Carlo integration; information theory and statistics; semi- and non-parametric modeling; time series and survival analysis.
Course may be repeated for credit with different instructor. Course may be repeated for credit when topic changes. Formerly known as 216A-216B and 217A-217B.
STAT 212B Topics in Theoretical Statistics 3 Units
Department: Statistics
Course level: Graduate
Terms course may be offered: Fall and spring
Grading: Letter grade.
Hours and format: 3 hours of Lecture per week for 15 weeks.
Prerequisites: 210 or 205 and 215.
This course introduces the student to topics of current research interest in theoretical statistics. Recent topics include information theory, multivariate analysis and random matrix theory, high-dimensional inference. Typical topics have been model selection; empirical and point processes; the bootstrap, stochastic search, and Monte Carlo integration; information theory and statistics; semi- and non-parametric modeling; time series and survival analysis.
Course may be repeated for credit with different instructor. Course may be repeated for credit when topic changes. Formerly known as 216A-216B and 217A-217B.
STAT 215A Statistical Models: Theory and Application 4 Units
Department: Statistics
Course level: Graduate
Terms course may be offered: Fall and spring
Grading: Letter grade.
Hours and format: 3 hours of Lecture and 2 hours of Laboratory per week for 15 weeks.
Prerequisites: Linear algebra, calculus, upper division probability and statistics, and familiarity with high-level programming languages. Statistics 133, 134, and 135 recommended.
Applied statistics with a focus on critical thinking, reasoning skills, and techniques. Hands-on-experience with solving real data problems with high-level programming languages such as R. Emphasis on examining the assumptions behind standard statistical models and methods. Exploratory data analysis (e.g., graphical data summaries, PCAs, clustering analysis). Model formulation, fitting, and validation and testing. Linear regression and generalizations (e.g., GLMs, ridge regression, lasso).
STAT 215B Statistical Models: Theory and Application 4 Units
Department: Statistics
Course level: Graduate
Terms course may be offered: Fall and spring
Grading: Letter grade.
Hours and format: 3 hours of Lecture and 2 hours of Laboratory per week for 15 weeks.
Prerequisites: Statistics 215A or consent of instructor.
Course builds on 215A in developing critical thinking skills and the techniques of advanced applied statistics. Particular topics vary with instructor. Examples of possible topics include planning and design of experiments, ANOVA and random effects models, splines, classification, spatial statistics, categorical data analysis, survival analysis, and multivariate analysis.
STAT 222 Masters of Statistics Capstone Project 4 Units
Department: Statistics
Course level: Graduate
Term course may be offered: Spring
Grading: Letter grade.
Hours and format: 4 hours of Seminar per week for 15 weeks.
Prerequisites: Statistics 201A-201B, 243. Restricted to students who have been admitted to the one-year Masters Program in Statistics beginning fall 2012 or later.
The capstone project is part of the masters degree program in statistics. Students engage in professionally-oriented group research under the supervision of a research advisor. The research synthesizes the statistical, computational, economic, and social issues involved in solving complex real-world problems.
STAT 230A Linear Models 4 Units
Department: Statistics
Course level: Graduate
Terms course may be offered: Fall and spring
Grading: Letter grade.
Hours and format: 3 hours of Lecture and 2 hours of Laboratory per week for 15 weeks.
Prerequisites: Matrix algebra, a year of calculus, two semesters of upper division or graduate probability and statistics.
Theory of least squares estimation, interval estimation, and tests under the general linear fixed effects model with normally distributed errors. Large sample theory for non-normal linear models. Two and higher way layouts, residual analysis. Effects of departures from the underlying assumptions. Robust alternatives to least squares.
STAT 232 Experimental Design 4 Units
Department: Statistics
Course level: Graduate
Terms course may be offered: Fall and spring
Grading: Letter grade.
Hours and format: 3 hours of Lecture and 2 hours of Laboratory per week for 15 weeks.
Prerequisites: 200B or equivalent.
Randomization, blocking, factorial design, confounding, fractional replication, response surface methodology, optimal design. Applications.
Course may be repeated for credit when topic changes.
STAT 239A The Statistics of Causal Inference in the Social Science 4 Units
Department: Statistics
Course level: Graduate
Terms course may be offered: Fall and spring
Grading: Letter grade. This is part one of a year long series course. A provisional grade of IP (in progress) will be applied and later replaced with the final grade after completing part two of the series.
Hours and format: 3 hours of lecture and 1 to 2 hours of discussion per week.
Prerequisites: At least one graduate matrix based multivariate regression course in addition to introductory statistics and probability.
Approaches to causal inference using the potential outcomes framework. Covers observational studies with and without ignorable treatment assignment, randomized experiments with and without noncompliance, instrumental variables, regression discontinuity, sensitivity analysis and randomization inference. Applications are drawn from a variety of fields including political science, economics, sociology, public health and medicine.
Instructor: Sekhon
STAT 239B Quantitative Methodology in the Social Sciences Seminar 4 Units
Department: Statistics
Course level: Graduate
Terms course may be offered: Fall and spring
Grading: Letter grade. This is part two of a year long series course. Upon completion, the final grade will be applied to both parts of the series.
Hours and format: 3 hours of lecture and 1 to 2 hours of discussion per week.
Prerequisites: Statistics 239A or equivalent.
A seminar on successful research designs and a forum for students to discuss the research methods needed in their own work, supplemented by lectures on relevant statistical and computational topics such as matching methods, instrumental variables, regression discontinuity, and Bayesian, maximum likelihood and robust estimation. Applications are drawn from political science, economics, sociology, and public health. Experience with R is assumed.
STAT C239A/POL SCI C236A The Statistics of Causal Inference in the Social Science 4 Units
Department: Statistics; Political Science
Course level: Graduate
Terms course may be offered: Fall and spring
Grading: Letter grade.
Hours and format: 3 hours of Lecture and 2 hours of Discussion per week for 15 weeks.
Approaches to causal inference using the potential outcomes framework. Covers observational studies with and without ignorable treatment assignment, randomized experiments with and without noncompliance, instrumental variables, regression discontinuity, sensitivity analysis and randomization inference. Applications are drawn from a variety of fields including political science, economics, sociology, public health and medicine.
STAT 240 Nonparametric and Robust Methods 4 Units
Department: Statistics
Course level: Graduate
Term course may be offered: Fall
Grading: Letter grade.
Hours and format: 3 hours of Lecture and 2 hours of Laboratory per week for 15 weeks.
Prerequisites: A year of upper division probability and statistics.
Standard nonparametric tests and confidence intervals for continuous and categorical data; nonparametric estimation of quantiles; robust estimation of location and scale parameters. Efficiency comparison with the classical procedures.
STAT C241A/COMPSCI C281A Statistical Learning Theory 3 Units
Department: Statistics; Computer Science
Course level: Graduate
Term course may be offered: Fall
Grading: Letter grade.
Hours and format: 3 hours of Lecture per week for 15 weeks.
Classification regression, clustering, dimensionality, reduction, and density estimation. Mixture models, hierarchical models, factorial models, hidden Markov, and state space models, Markov properties, and recursive algorithms for general probabilistic inference nonparametric methods including decision trees, kernal methods, neural networks, and wavelets. Ensemble methods.
Instructors: Bartlett, Jordan, Wainwright
STAT C241B/COMPSCI C281B Advanced Topics in Learning and Decision Making 3 Units
Department: Statistics; Computer Science
Course level: Graduate
Term course may be offered: Spring
Grading: Letter grade.
Hours and format: 3 hours of Lecture per week for 15 weeks.
Recent topics include: Graphical models and approximate inference algorithms. Markov chain Monte Carlo, mean field and probability propagation methods. Model selection and stochastic realization. Bayesian information theoretic and structural risk minimization approaches. Markov decision processes and partially observable Markov decision processes. Reinforcement learning.
Instructors: Bartlett, Jordan, Wainwright
STAT 243 Introduction to Statistical Computing 4 Units
Department: Statistics
Course level: Graduate
Term course may be offered: Fall
Grading: Letter grade.
Hours and format: 3 hours of Lecture and 2 hours of Laboratory per week for 15 weeks.
Prerequisites: Graduate standing.
Concepts in statistical programming and statistical computation, including programming principles, data and text manipulation, parallel processing, simulation, numerical linear algebra, and optimization.
Student Learning Outcomes: Become familiar with concepts and tools for reproducible research and good scientific computing practices.^Operate effectively in a UNIX environment and on remote servers.^Program effectively in languages including R and Python with an advanced knowledge of language functionality and an understanding of general programming concepts.^Understand in depth and make use of principles of numerical linear algebra, optimization, and simulation for statistics-related research.
Course may be repeated for credit. Course may be repeated for credit when topic changes.
STAT 244 Statistical Computing 4 Units
Department: Statistics
Course level: Graduate
Term course may be offered: Spring
Grading: Letter grade.
Hours and format: 3 hours of Lecture and 2 hours of Laboratory per week for 15 weeks.
Prerequisites: Knowledge of a higher level programming language.
Algorithms in statistical computing: random number generation, generating other distributions, random sampling and permutations. Matrix computations in linear models. Non-linear optimization with applications to statistical procedures. Other topics of current interest, such as issues of efficiency, and use of graphics.
STAT C245A/PB HLTH C240A Biostatistical Methods: Advanced Categorical Data Analysis 4 Units
Department: Statistics; Public Health
Course level: Graduate
Term course may be offered: Fall. Offered odd-numbered years.
Grading: Letter grade.
Hours and format: 3 hours of Lecture and 2 hours of Laboratory per week for 15 weeks.
Prerequisites: Statistics 200A (may be taken concurrently).
This course focuses on statistical methods for discrete data collected in public health, clinical and biological studies. Lectures topics include proportions and counts, contingency tables, logistic regression models, Poisson regression and log-linear models, models for polytomous data and generalized linear models. Computing techniques, numerical methods, simulation and general implementation of biostatistical analysis techniques with emphasis on data applications.
STAT C245B/PB HLTH C240B Biostatistical Methods: Survival Analysis and Causality 4 Units
Department: Statistics; Public Health
Course level: Graduate
Term course may be offered: Spring. Offered even-numbered years.
Grading: Letter grade.
Hours and format: 3 hours of Lecture and 2 hours of Laboratory per week for 15 weeks.
Prerequisites: Statistics 200B (may be taken concurrently).
Analysis of survival time data using parametric and non-parametric models, hypothesis testing, and methods for analyzing censored (partially observed) data with covariates. Topics include marginal estimation of a survival function, estimation of a generalized multivariate linear regression model (allowing missing covariates and/or outcomes), estimation of a multiplicative intensity model (such as Cox proportional hazards model) and estimation of causal parameters assuming marginal structural models. General theory for developing locally efficient estimators of the parameters of interest in censored data models. Computing techniques, numerical methods, simulation and general implementation of biostatistical analysis techniques with emphasis on data applications.
Instructor: van der Laan
STAT C245C/PB HLTH C240C Biostatistical Methods: Computational Statistics with Applications in Biology and Medicine 4 Units
Department: Statistics; Public Health
Course level: Graduate
Term course may be offered: Fall. Offered even-numbered years.
Grading: Letter grade.
Hours and format: 3 hours of Lecture and 2 hours of Laboratory per week for 15 weeks.
Prerequisites: Statistics 200A or equivalent (may be taken concurrently).
This course provides an introduction to computational statistics, with emphasis on statistical methods and software for addressing high-dimensional inference problems in biology and medicine. Topics include numerical and graphical data summaries, loss-based estimation (regression, classification, density estimation), smoothing, EM algorithm, Markov chain Monte-Carlo, clustering, multiple testing, resampling, hidden Markov models, in silico experiments.
Instructor: Dudoit
STAT C245D/PB HLTH C240D Biostatistical Methods: Applications of Statistics to Genetics and Molecular Biology 4 Units
Department: Statistics; Public Health
Course level: Graduate
Terms course may be offered: Fall and spring
Grading: Letter grade.
Hours and format: 3 hours of lecture and 2 hours of laboratory per week.
Prerequisites: Statistics 200A-200B or Statistics 201A-201B (may be taken concurrently) or consent of instructor.
This course and Pb Hlth C240C/STAT C245C provide an introduction to computational statistics with emphasis on statistical methods and software for addressing high-dimensional inference problems that arise in current biological and medical research. The courses also discusses statistical computing resources, with emphasis on the R language and environment (www.r-project.org). Programming topics to be discussed include: data structures, functions, statistical models, graphical procedures, designing an R package, object-oriented programming, inter-system interfaces. The statistical and computational methods are motivated by and illustrated on data structures that arise in current high-dimensional inference problems in biology and medicine.
Instructor: Dudoit
STAT C245E/PB HLTH C240E Statistical Genomics 4 Units
Department: Statistics; Public Health
Course level: Graduate
Term course may be offered: Spring
Grading: Letter grade.
Hours and format: 3 hours of Lecture and 1 hour of Discussion per week for 15 weeks.
Prerequisites: Statistics 200A and 200B or equivalent (may be taken concurrently). A course in algorithms and knowledge of at least one computing language (e.g., R, matlab) is recommended.
Genomics is one of the fundamental areas of research in the biological sciences and is rapidly becoming one of the most important application areas in statistics. This is the first course of a two-semester sequence, which provides an introduction to statistical and computational methods for the analysis of meiosis, population genetics, and genetic mapping. The second course is Statistics C245F/Public Health C240F. The courses are primarily intended for graduate students and advanced undergraduate students from the mathematical sciences.
Instructors: Dudoit, Huang, Nielsen, Song
STAT C245F/PB HLTH C240F Statistical Genomics 4 Units
Department: Statistics; Public Health
Course level: Graduate
Term course may be offered: Spring
Grading: Letter grade.
Hours and format: 3 hours of Lecture and 1 hour of Discussion per week for 15 weeks.
Genomics is one of the fundamental areas of research in the biological sciences and is rapidly becoming one of the most important application areas in statistics. The first course in this two-semester sequence is Public Health C240E/Statistics C245E. This is the second course, which focuses on sequence analysis, phylogenetics, and high-throughput microarray and sequencing gene expression experiments. The courses are primarily intended for graduate students and advanced undergraduate students from the mathematical sciences.
Instructors: Dudoit, Huang, Nielsen, Song
STAT C247C/PB HLTH C242C Longitudinal Data Analysis 4 Units
Department: Statistics; Public Health
Course level: Graduate
Term course may be offered: Spring. Offered even-numbered years.
Grading: Letter grade.
Hours and format: 3 hours of Lecture and 2 hours of Discussion per week for 15 weeks.
Prerequisites: 142, 145, 241 or equivalent courses in basic statistics, linear and logistic regression.
The course covers the statistical issues surrounding estimation of effects using data on subjects followed through time. The course emphasizes a regression model approach and discusses disease incidence modeling and both continuous outcome data/linear models and longitudinal extensions to nonlinear models (e.g., logistic and Poisson). The primary focus is from the analysis side, but mathematical intuition behind the procedures will also be discussed. The statistical/mathematical material includes some survival analysis, linear models, logistic and Poisson regression, and matrix algebra for statistics. The course will conclude with an introduction to recently developed causal regression techniques (e.g., marginal structural models). Time permitting, serially correlated data on ecological units will also be discussed.
Instructors: Hubbard, Jewell
STAT 248 Analysis of Time Series 4 Units
Department: Statistics
Course level: Graduate
Terms course may be offered: Fall and spring
Grading: Letter grade.
Hours and format: 3 hours of Lecture and 2 hours of Laboratory per week for 15 weeks.
Prerequisites: 102 or equivalent.
Frequency-based techniques of time series analysis, spectral theory, linear filters, estimation of spectra, estimation of transfer functions, design, system identification, vector-valued stationary processes, model building.
STAT C249A/PB HLTH C246A Censored Longitudinal Data and Causality 4 Units
Department: Statistics; Public Health
Course level: Graduate
Term course may be offered: Spring. Offered odd-numbered years.
Grading: Letter grade.
Hours and format: 3 hours of Lecture and 2 hours of Laboratory per week for 15 weeks.
Prerequisites: 240B, Statistics 200A-200B or consent of instructor.
This course examines optimal robust methods for statistical inference regarding causal and non-causal parameters based on longitudinal data in the presence of informative censoring and informative confounding of treatment. Models presented include multivariate regression models, multiplicative intensity models for counting processes, and causal models such as marginal structural models and structural nested models. Methods will be illustrated with data sets of practical interest and analyzed in the laboratory section. This course, appropriate for advanced masters and Ph.D. students, provides exposure to a number of ongoing research topics.
Instructor: van der Laan
STAT 260 Topics in Probability and Statistics 3 Units
Department: Statistics
Course level: Graduate
Terms course may be offered: Fall and spring
Grading: Letter grade.
Hours and format: 3 hours of Lecture per week for 15 weeks.
Special topics in probability and statistics offered according to student demand and faculty availability.
Course may be repeated for credit. Course may be repeated for credit when topic changes.
STAT C261/SOCIOL C271D Quantitative/Statistical Research Methods in Social Sciences 3 Units
Department: Statistics; Sociology
Course level: Graduate
Terms course may be offered: Fall and spring
Grading: Letter grade.
Hours and format: 2 hours of Lecture per week for 15 weeks.
Prerequisites: Consent of instructor.
Selected topics in quantitative/statistical methods of research in the social sciences and particularly in sociology. Possible topics include: analysis of qualitative/categorical data; loglinear models and latent-structure analysis; the analysis of cross-classified data having ordered and unordered categories; measure, models, and graphical displays in the analysis of cross-classified data; correspondence analysis, association analysis, and related methods of data analysis.
STAT 272 Statistical Consulting 3 Units
Department: Statistics
Course level: Graduate
Terms course may be offered: Fall and spring
Grading: Offered for satisfactory/unsatisfactory grade only.
Hours and format: 2 hours of session per week and individual meetings as necessary.
Prerequisites: Some course work in applied statistics and permission of instructor.
To be taken concurrently with service as a consultant in the department's drop-in consulting service. Participants will work on problems arising in the service and will discuss general ways of handling such problems. There will be working sessions with researchers in substantive fields and occasional lectures on consulting.
Course may be repeated for credit. Course may be repeated for credit when topic changes.
STAT 278B Statistics Research Seminar 1 - 4 Units
Department: Statistics
Course level: Graduate
Terms course may be offered: Fall and spring
Grading: Offered for satisfactory/unsatisfactory grade only.
Hours and format: 2 or more hours of seminar per week.
Special topics, by means of lectures and informational conferences.
Course may be repeated for credit. Course may be repeated for credit when topic changes.
STAT 298 Directed Study for Graduate Students 1 - 12 Units
Department: Statistics
Course level: Graduate
Terms course may be offered: Fall, spring and summer
Grading: Letter grade.
Hours and format: Zero hours of Independent study per week for 15 weeks. 1 to 12 hour of Independent study per week for 8 weeks. 1 to 16 hour of Independent study per week for 6 weeks.
Prerequisites: Consent of instructor.
Special tutorial or seminar on selected topics.
Course may be repeated for credit. Course may be repeated for credit when topic changes.
STAT 299 Individual Study Leading to Higher Degrees 1 - 12 Units
Department: Statistics
Course level: Graduate
Terms course may be offered: Fall, spring and summer
Grading: Letter grade.
Hours and format: Work hours to be arrange based on unit value. Offered for 1-6 units during Summer Session.
Individual study
Course may be repeated for credit. Course may be repeated for credit when topic changes.
STAT 375 Professional Preparation: Teaching of Probability and Statistics 2 - 4 Units
Department: Statistics
Course level: Professional course for teachers or prospective teachers
Terms course may be offered: Fall and spring
Grading: Offered for satisfactory/unsatisfactory grade only.
Hours and format: 1 or 2 hours of lecture and 2 to 4 of laboratory per week.
Prerequisites: Graduate standing and appointment as a graduate student instructor.
Discussion, problem review and development, guidance of laboratory classes, course development, supervised practice teaching.
Course may be repeated for credit. Course may be repeated for credit when topic changes. Formerly known as Statistics 300.
STAT 601 Individual Study for Master's Candidates 1 - 8 Units
Department: Statistics
Course level: Graduate examination preparation
Terms course may be offered: Fall, spring and summer
Grading: Offered for satisfactory/unsatisfactory grade only.
Hours and format: By appointment.
Individual study in consultation with the graduate adviser, intended to provide an opportunity for qualified students to prepare themselves for the master's comprehensive examinations. Units may not be used to meet either unit or residence requirements for a master's degree.
Course may be repeated for a maximum of 16 units.Course may be repeated for a maximum of 16 units.
STAT 602 Individual Study for Doctoral Candidates 1 - 8 Units
Department: Statistics
Course level: Graduate examination preparation
Terms course may be offered: Fall, spring and summer
Grading: Offered for satisfactory/unsatisfactory grade only.
Hours and format: Zero hours of Independent study per week for 15 weeks. 1 to 8 hour of Independent study per week for 8 weeks. 1 to 10 hour of Independent study per week for 6 weeks.
Prerequisites: One year of full-time graduate study and permission of the graduate adviser.
Individual study in consultation with the graduate adviser, intended to provide an opportunity for qualified students to prepare themselves for certain examinations required of candidates for the Ph.D. degree.
Course may be repeated for a maximum of 16 units.Course may be repeated for a maximum of 16 units. Course does not satisfy unit or residence requirements for doctoral degree.
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