Statistics Course Listings

Lower Division Courses

10. Introduction to Statistical Reasoning. (5)  (Formerly numbered 10A.) Lecture, two hours; discussion, one hour; computer laboratory, two hours. Preparation: three years of high school mathematics. Not open for credit to students with credit for course 10H, 11, 12, 13, or 14 (or former Anthropology M80, Economics M40, Geography M40, Sociology M18, Statistics 10A, M11, or M12). Introduction to statistical thinking and understanding, including strengths and limitations of basic experimental designs, graphical and numerical summaries of data, inference, regression as descriptive tool. P/NP or letter grading.

10H. Introduction to Statistical Reasoning (Honors). (4)  Lecture, three hours; discussion, two hours. Preparation: three years of high school mathematics. Not open for credit to students with credit for course 10, 11, M12, 13, Anthropology M80, Geography M40, or Sociology M18 (or former Statistics M11, M13, Economics M40, or Organismic Biology M22). Descriptive statistics, elementary probability, random variables, binomial and normal distributions. Large and small sample inference concerning means. Introduction to statistical software. Letter grading.

11. Introduction to Statistical Methods for Business and Economics. (5)  Lecture, three hours; discussion, one hour; computer laboratory, one hour. Requisite or corequisite: Mathematics 3A or 31A. Not open for credit to students with credit for course 10, 10A, 10H, M12, 13, 14, 100A, 100B, 100C, Anthropology M80, Geography M40, Mathematics 170A, 170B, or Sociology M18 (or former Statistics M11, M13, or Organismic Biology M22). Elements of statistical analysis. Presentation and interpretation of data; descriptive statistics; theory of probability and basic sampling distributions; statistical inference, including principles of estimation and tests of hypotheses; introduction to regression and correlation. P/NP or letter grading.

12. Introduction to Statistical Methods for Geography and Environmental Studies. (5)  (Formerly numbered M12.) Lecture, four hours; discussion, one hour; laboratory, one hour. Not open for credit to students with credit for course 10, 11, or 13 (or former Statistics M11, Anthropology M80, Economics M40, Geography M40, or Sociology M18). Introduction to statistical thinking and understanding, with emphasis on techniques used in geography and environmental science. Underlying logic behind statistical procedures, role of variation in statistical thinking, strengths and limitations of statistical summaries, and fundamental inferential tools. Emphasis on applications in geography and environmental science in laboratory work using professional statistical analysis package, including spatial statistics. P/NP or letter grading.

13. Introduction to Statistical Methods for Life and Health Sciences. (5)  Lecture, three hours; discussion, one hour; laboratory, one hour. Not open for credit to students with credit for course 10, 10A, 10H, 11, M12, 14, Anthropology M80, Geography M40, or Sociology M18 (or former Statistics M11, Economics M40, or Organismic Biology M22). Presentation and interpretation of data, descriptive statistics, introduction to correlation and regression and to basic statistical inference (estimation, testing of means and proportions, ANOVA) using both bootstrap methods and parametric models. P/NP or letter grading.

14. Introduction to Statistical Methods in Physical Sciences and Engineering. (5)  Lecture, three hours; discussion, one hour; laboratory, one hour. Requisite: Mathematics 31A. Not open for credit to students with credit for course 10, 10A, 10H, 11, M12, 13, Anthropology M80, Geography M40, or Sociology M18 (or former Statistics M11, M13, Economics M40, or Organismic Biology M22). Introduction to conceptual and technical aspects of statistics, with attention to applications of physical sciences and engineering. Topics include data collection and experimental design, quantifying uncertainty in measurement, descriptive statistics, introduction to time series and regression. Laboratory component to learn data analysis on real data and fundamental techniques of computer statistical analysis, including bootstrap methods. P/NP or letter grading.

35A. Interactive and Computational Probability. (4)  (Formerly numbered 35.) Lecture, three hours; discussion, one hour. Not open for credit to students with credit for course 35B. Basic introductory probability topics in interactive problem-driven manner. Various applets, interfaces, and demonstrations used to illustrate fundamental properties of distributions, random number generation, combinatorics, expectation, variability, and sampling. Assignment of projects that require light computer programming. Emphasis on practical description, utilization, and graphical presentation of various probabilistic modeling techniques. P/NP or letter grading.

35B. Introduction to Probability with Applications to Poker. (4)  Lecture, three hours; discussion, one hour. Not open for credit to students with credit for course 35A. Exploration of some main topics in introductory probability theory, especially discrete probability problems, that are useful in wide variety of scientific applications. Topics include conditional probability and conditional expectation, combinatorics, laws of large numbers, central limit theorem, Bayes theorem, univariate distributions, Markov processes, and Brownian motion. Examination of computer simulation in depth and discussion of computational approximations of solutions to complex problems using R, with examples of situations and concepts that arise naturally when playing Texas Hold'em and other games. P/NP or letter grading.

35C. Applied Sampling. (4)  (Formerly numbered 34.) Lecture, three hours; discussion, one hour. Designed for lower division students in social or life sciences and those who plan to major in Statistics. Topics include methods of sampling from finite populations, sources of sampling and estimation bias, and methods of generating efficient and precise estimates of population characteristics. Practical applications of sampling methods via lectures and hands-on laboratory exercises. P/NP or letter grading.

88. Sophomore Seminars: Statistics. (2)  Seminar, two hours. Requisite: one course from 10, 10A, 10H, 11, M12, 13, 14, Anthropology M80, Geography M40, or Sociology M18. Limited to 20 lower division students. Readings and discussions designed to introduce students to current statistical consulting research and fieldwork disciplines. Culminating project may be required. P/NP or letter grading.

Upper Division Courses

100A. Introduction to Probability. (4)  Lecture, three hours; discussion, one hour. Recommended preparation: course 35A or 35B or 35C. Requisites: Mathematics 32B, 33A. Not open to students with credit for Electrical Engineering 131A or Mathematics 170A; open to graduate students. Students may receive credit for only two of following: courses 100A, 110A, Biostatistics 100A. Probability distributions, random variables, vectors, and expectation. P/NP or letter grading.

100B. Introduction to Mathematical Statistics. (4)  Lecture, three hours; discussion, one hour. Requisite: course 100A. Survey sampling, estimation, testing, data summary, one- and two-sample problems. P/NP or letter grading.

100C. Linear Model with Experimental Design. (4)  Lecture, three hours; discussion, one hour. Requisite: course 100B. Theory of linear models, with emphasis on matrix approach to linear regression. Topics include model fitting, extra sums of squares principle, testing general linear hypothesis in regression, inference procedures, Gauss/Markov theorem, examination of residuals, principle component regression, stepwise procedures. P/NP or letter grading.

101A. Introduction to Design and Analysis of Experiment. (4)  Lecture, three hours; discussion, one hour. Requisites: one course from 10, 11, 12, 13, or 14, and Mathematics 32B. Fundamentals of collecting data, including components of experiments, randomization and blocking, completely randomized design and ANOVA, multiple comparisons, power and sample size, and block designs. P/NP or letter grading.

101B. Introduction to Data Analysis and Regression. (4)  (Formerly numbered 120A.) Lecture, three hours; discussion, one hour. Requisites: course 35A or 35B or 35C and Mathematics 3B or 31B, or Mathematics 32B and 33A. Recommended: course 110A. Designed for juniors/seniors. Applied regression analysis, with emphasis on general linear model (e.g., multiple regression) and generalized linear model (e.g., logistic regression). Special attention to modern extensions of regression, including regression diagnostics, graphical procedures, and bootstrapping for statistical influence. P/NP or letter grading.

101C. Introduction to Regression and Data Mining. (4)  (Formerly numbered 120B.) Lecture, three hours; discussion, one hour. Enforced requisite: course 101B. Designed for juniors/seniors. Applied regression analysis, with emphasis on general linear model (e.g., multiple regression) and generalized linear model (e.g., logistic regression). Special attention to modern extensions of regression, including regression diagnostics, graphical procedures, and bootstrapping for statistical influence. P/NP or letter grading.

102A. Introduction to Computational Statistics with R. (4)  (Formerly numbered 135.) Lecture, three hours. Requisites: course 35A or 35B or 35C and Mathematics 3B or 31B, or Mathematics 32B and 33A. Introductory examination of programming in R. P/NP or letter grading.

102B. Matrix Computation and Optimization for Statistics. (4)  (Formerly numbered 175.) Lecture, three hours. Requisite: one course from 10, 11, 12, 13, 14, 100A, or 110A. Introduction to those parts of matrix algebra and matrix computation that are most useful for statisticians. Use of computer exercises and R programming language. P/NP or letter grading.

102C. Markov Chain Monte Carlo Methods: Introduction. (4)  Lecture, three hours. Requisite: course 100B. Introduction to Markov chain Monte Carlo (MCMC) algorithms for scientific computing. Generation of random numbers from specific distribution. Rejection and importance sampling and its role in MCMC. Markov chain theory and convergence properties. Metropolois and Gibbs sampling algorithms. Extensions as simulated tempering. Theoretical understanding of methods and their implementation in concrete computational problems. P/NP or letter grading.

105. Statistics for Engineers. (4)  Lecture, three hours; discussion, one hour. Requisite: course 100A or Electrical Engineering 131A or Mathematics 170A. Mathematical foundation of basic concepts and techniques of statistics. Topics include joint distributions, limit theorems, maximum likelihood estimation, and hypothesis testing (including Neyman/Pearson paradigm and likelihood ratio tests), with emphasis on application of these concepts. Discussion of means for checking whether assumptions required for mathematical foundations are appropriate for given set of data. P/NP or letter grading.

110A-110B. Applied Statistics. (4-4)  Lecture, three hours; discussion, one hour. P/NP or letter grading. 110A. Requisites: course 35A or 35B or 35C and Mathematics 3B or 31B, or Mathematics 32B and 33A. Not open to students with credit for Electrical Engineering 131A. Students may receive credit for only two of following: courses 100A, 110A, Biostatistics 100A. Probability, distributions, expectation, estimation, central limit theorem, confidence intervals, testing. 110B. Requisite: course 110A. One- and two-sample problems, goodness of fit and contingency tables, correlation and regression, analysis of variance, nonparametrics.

112. Statistical Methods for Social Sciences. (5)  Lecture, three hours; discussion, one hour. Requisite: course 10. Limited to juniors/seniors. Statistical methods in social sciences, including regression, multivariate techniques, logistic regression, and data-handling and analysis. Applications to social sciences, using professional statistical analysis software package for data analysis. Letter grading.

130A. Statistical Analysis with STATA. (4)  Lecture, three hours; discussion, one hour. Requisite: one course from 10, 10A, 10H, 11, M12, 13, 14, 100A, or 110A. How to manage and analyze quantitative data using STATA statistical software. Graphical analysis and programming and extensions to basic package. P/NP or letter grading.

130B. Statistical Analysis with SAS. (4)  Lecture, three hours. Requisite: one course from 10, 10A, 10H, 11, M12, 13, 14, 100A, or 110A. How to manage and analyze quantitative data using statistical procedures produced by Statistical Analysis System (SAS) Institute, Inc. Discussion of many statistical techniques available in SAS and ways to extend basic system by SAS programming. P/NP or letter grading.

130C. Statistical Analysis with SPSS. (4)  Lecture, three hours. Requisite: one course from 10, 10A, 10H, 11, M12, 13, 14, 100A, or 110A. Overview of Statistical Package for Social Sciences (SPSS) software intended for students in any major who have interest in data analysis. Though original design catered to students in social sciences, current development has considerably wider application, with vast range of functionality from simple to more advanced data manipulation and analysis. Ease of use maintained that is popular with students not accustomed to statistical programming. Ability of program to combine ease of use with varied levels of data exploration and inference has made it popularly used analytical tool. P/NP or letter grading.

130D. Statistical Programming, Computation, and Visualization in C/C++/VTK. (4)  Lecture, three hours. Requisite: Program in Computing 10A or 10B or 10C or 20A. Intermediate programming and computation course, with emphasis on statistical and visualization aspects of research in biomedical, optical imaging, and high-dimensional data analysis. P/NP or letter grading.

140SL. Practice of Statistical Consulting. (4)  Lecture, one hour; discussion, two hours. Enforced requisites: courses 88, 100B, 101B, one course from 130A through 130D. Limited to seniors. Opportunity to solve real data analysis problems for real community-based or campus-based clients. Students work in small groups with faculty member and client to frame client's question in statistical terms, create statistical model, analyze data, and report results. Weekly meetings in classroom setting to study basic consulting skills, share experiences, exchange ideas, and make reports. On-site visits as necessary. Courses 140SL and 141SL must be taken in consecutive terms. In Progress grading (credit to be given only on completion of course 141SL).

141SL. Practice of Statistical Consulting. (4)  Seminar, one hour; research group meeting, two hours. Enforced requisite: course 140SL. Limited to seniors. Opportunity to solve real data analysis problems for real community-based or campus-based clients. Students work in small groups with faculty member and client to frame client's question in statistical terms, create statistical model, analyze data, and report results. Weekly meetings in classroom setting to study basic consulting skills, share experiences, exchange ideas, and make reports. On-site visits as necessary. Courses 140SL and 141SL must be taken in consecutive terms. Letter grading.

150. Data Analysis. (4)  Lecture, three hours. Requisites: courses 100A and 100B, or 101B and 101C, or 110A and 110B, or one course from 10, 11, 12, 13 and one upper division statistics course. Practice in solving statistical problems, with coverage of basics of cleaning and checking data, exploratory analysis, model building, model checking, reporting results, working with "clients." P/NP or letter grading.

C151. Experimental Design. (4)  (Formerly numbered C125.) Lecture, three hours. Requisite: course 100C or 101B or 110B. Basic principles, analysis of variance, randomized block designs, Latin squares, balanced incomplete block designs, factorial designs, fractional factorial designs, minimum aberration designs, robust parameter designs. Concurrently scheduled with course C225. P/NP or letter grading.

C152. Bootstrap, Jackknife, and Resampling Methods. (4)  (Formerly numbered C126.) Lecture, three hours; discussion, one hour. Requisite: one course from 10, 10H, 11, 12, 13, 14, 100A, or 110A. Simple intuitive introduction to practical application of statistics for experiments and surveys in business and biological, medical, physical, and social sciences. Resampling methods -- bootstrap and permutation test -- are table-free and distribution-free, require common sense (not calculus), yet have broader range of applications than classical parametric statistical procedures. Concurrently scheduled with course C226. P/NP or letter grading.

153. Statistical Analysis with Missing Data. (4)  Lecture, three hours. Requisite: course 102A. Study of methods dealing with nonresponse and missing data, including introduction to terminology, limitations of simple methods, and modern methods for dealing with missing data, such as EM algorithm and multiple imputation. P/NP or letter grading.

M154. Measurement and Its Applications. (4)  (Same as Psychology M144.) Lecture, three hours. Requisites: courses 10, 11, M12, 13, 14, Psychology 100A. Selected theories for quantification of psychological, educational, social, and behavioral science data. Classical test, factor analysis, generalizability, item response, optimal scaling, ordinal measurement, computer-adaptive, and related theories. Construction of tests and measures and their reliability, validity, and bias. P/NP or letter grading.

C155. Introduction to Statistical Analysis of Environmental Data. (4)  Lecture, three hours. Requisite: course 10. Routine intermediate applied statistics course, with emphasis on applications to environmental data and statistical computing with the language R. Statistical analysis and scientific report from real data required. Concurrently scheduled with course CM255. P/NP or letter grading.

C156. Data Management. (4)  Lecture, three hours. Requisite: one course from 10, 11, 12, 13, or 14. Proper methods by which researchers should create, document, maintain, and utilize statistical databases. Basics of raw data formats to completion of data archive. Concurrently scheduled with course C235. P/NP or letter grading.

158. Statistical Analysis of Internet and World Wide Web Data. (4)  (Formerly numbered C158.) Lecture, three hours. Requisite: course 100B or 100C or 101B. Demography and statistical models of browsing behavior of World Wide Web users, models of Internet traffic data, and statistics methods for creating better Web search engines and spam filters. Use of large data sets to illustrate important issues and statistical solutions. Statistical software, some programming, handling of large data sets, and text mining, with emphasis on acquiring hands-on experience and on becoming active participants in current research debates. P/NP or letter grading.

C160. Site-Specifics Topics. (4)  Seminar, three hours. Tracking of invisible flows of data through greater Los Angeles metropolitan area, with focus on small number of specific sites situated prominently in both physical and virtual (data) spaces. Documentation of kinds of data that originate, terminate, or simply route through each location. Consideration of analyses (visual, computational, or simply informal), decisions that are made, and actions that are taken on basis of these data, whether they be human or automated responses. Documentation of how patterns of data acquisition and analysis dictate behaviors, enable or restrict movements, and shape local community. Alterations or additions to data flows that could improve quality of life for inhabitants of or visitors to sites. May be repeated for credit; however, only one C160 may be applied toward major or minor requirements. Concurrently scheduled with course C260. P/NP or letter grading.

161. Introduction to Pattern Recognition and Machine Learning. (4)  Lecture, three hours. Requisites: course 100B, Mathematics 33A. Introduction to fundamental concepts, theories, and algorithms for pattern recognition and machine learning that are useful for statistics modeling, image analysis, speech recognition, data mining, and computational biology. Topics include Bayesian decision theory, parametric and nonparametric learning, data clustering, dimension reduction, Adaboosting. May not be applied toward M.S. or Ph.D. requirements. P/NP or letter grading.

165. Statistical Methods and Data Mining. (4)  Lecture, three hours. Requisite: course 100A. Introduction and overview of up-to-date statistical methods in microarray analysis designed for students in biostatistics, statistics, and human genetics who are interested in technology and statistical analysis of microarray experiments. Useful for biology students with basic statistical training who are interested in understanding logic underlying many statistical methods. P/NP or letter grading.

170. Introduction to Time-Series Analysis. (4)  Lecture, three hours; discussion, one hour. Requisite: course 100C or 110B or 120A. Exploration of standard methods in temporal and frequency analysis used in analysis of numerical time-series data. Examples provided throughout, and students implement techniques discussed. P/NP or letter grading.

M171 Introduction to Spatial Statistics. (4)  (Same as Geography M171.) Lecture, three hours; laboratory, one hour. Requisite: one course from 10, 11, 12, 13, or 14. Introduction to methods of measurement and interpretation of geographic distributions and associations. P/NP or letter grading.

C180. Introduction to Bayesian Statistics. (4)  Lecture, three hours; discussion, one hour. Requisites: Mathematics 32B, 33B. Designed for juniors/seniors. Introduction to statistical inference based on use of Bayes theorem, covering foundational aspects, current applications, and computational issues. Topics include Stein paradox, nonparametric Bayes, and statistical learning. Examples of applications include protein alignment algorithms and image denoising procedures. May not be applied toward Ph.D. in Statistics. Concurrently scheduled with course C236. P/NP or letter grading.

C182. Fundamentals of Scientific Writing. (2)  Seminar, one hour. Development and perfection of student written communication skills through variety of scientific writing and reading assignments. Objectives and techniques of scientific writing and practice with different forms of professional writing. Analysis of quality of writing, including control, clarity, grammar, and mechanics. Concurrently scheduled with course C295. P/NP or letter grading.

C183. Statistical Models in Finance. (4)  Lecture, three hours. Requisite: course 100B or 110B. Designed for juniors/seniors and graduate students. Statistical techniques in investment theory using real market data. Portfolio management, risk diversification, efficient frontier, single index model, capital asset pricing model (CAPM), beta of a stock, European and American options (Black/Scholes model, binomial model). Concurrently scheduled with course C283. P/NP or letter grading.

C184. Scientific Writing. (2)  Seminar, one hour. Development of oral and written presentations of statistical data. Objectives and techniques of scientific writing and practice with different forms of professional writing. Participation in oral presentations of student work. Concurrently scheduled with course C294. P/NP or letter grading.

CM185. Statistical Methods for Physical Sciences. (4)  (Same as Atmospheric and Oceanic Sciences CM185.) Lecture, three hours. Designed for juniors/seniors. Statistical framework for data analysis in fields of atmospheric sciences, astronomy, geology, and chemistry, depending on class composition. Presentation of popular techniques in all fields, with emphasis on applications and data, not theory, although some understanding of theory is needed. Concurrently scheduled with course CM252. P/NP or letter grading.

186. Careers in Statistics. (1)  Seminar, one hour. Discussion of applications of statistics by weekly guest speakers. How statistics is applied to legal questions, economic decisions, arts, environment, and other fields, with some emphasis on career paths in statistics. P/NP grading.

187. Current Topics in Statistics. (4)  (Formerly numbered 197.) Lecture, three hours. Limited to juniors/seniors. Study of selected current topics in statistics. May not be repeated. P/NP or letter grading.

195. Community or Corporate Internship in Statistics. (4)  Tutorial, four hours. Limited to juniors/seniors. Internship in supervised setting in community agency or business. Students meet on regular basis with instructor and provide periodic reports of their experience. Individual contract with supervising faculty member required. P/NP or letter grading.

199. Directed Research in Statistics. (1 to 4)  Tutorial, one hour. Limited to juniors/seniors. Supervised individual research or investigation under guidance of faculty mentor. Culminating paper or project required. Individual contract required. P/NP or letter grading.

Graduate Courses

200A. Applied Probability. (4)  (Formerly numbered M220B.) Lecture, three hours. Requisite: course 100A or Mathematics 170A. Limited to graduate statistics students. Simulation, renewal theory, martingale, and selected topics from queuing, reliability, speech recognition, computational biology, mathematical finance, epidemiology. S/U or letter grading.

200B. Theoretical Statistics. (4)  (Formerly numbered 200A.) Lecture, three hours. Sufficiency, exponential families, least squares, maximum likelihood estimation, Bayesian estimation, Fisher information, Cramér/Rao inequality, Stein's estimate, empirical Bayes, shrinkage and penalty, confidence intervals. Likelihood ratio test, p-value, false discovery, nonparametrics, semi-parametrics, model selection, dimension reduction. S/U or letter grading.

200C. Large Sample Theory, Including Resampling. (4)  (Formerly numbered 200B.) Lecture, three hours. Enforced requisite: course 200B. Asymptotic properties of tests and estimates, consistency and efficiency, likelihood ratio tests, chi-squared tests. S/U or letter grading.

201A. Research Design, Sampling, and Data Management. (4)  (Formerly numbered M220A.) Lecture, three hours. Designed for graduate students. Conditioning, Markov chains, Poisson process, Brownian motion, stationary processes, applications. S/U or letter grading.

201B. Regression Analysis: Model Building, Fitting, and Criticism. (4)  (Formerly numbered C217A.) Lecture, three hours. Enforced requisite: course 201A. Designed for graduate students. Applied regression analysis, with emphasis on general linear model (e.g., multiple regression) and generalized linear model (e.g., logistic regression). Special attention to modern extensions of regression, including regression diagnostics, graphical procedures, and bootstrapping for statistical inference. S/U or letter grading.

201C. Advanced Modeling and Data mining. (4)  (Formerly numbered C217B.) Lecture, three hours. Enforced requisite: course 201B. Designed for graduate students. Building on tools of regression analysis (model fitting and criticism), exploration of recent advances in computer-intensive methods. Consideration of ensemble methods, techniques for data mining, and variety of other approaches that have emerged at boundaries between statistics, computer science, and machine learning. S/U or letter grading.

202A. Statistics Programming. (4)  (Formerly numbered 210A.) Lecture, three hours. Designed for graduate students. Outline of principles of applied statistics, followed by survey of specific data analyses from physical, life, and social sciences. Methods include regression, analysis of variance and covariance, survival analysis, categorical data analysis, and simple time-series analysis. Illustration of transformations, plotting, model selection and evaluation, and estimation and decision procedures. S/U or letter grading.

202B. Numerical Linear Algebra and Random Numbers. (4)  (Formerly numbered 210B.) Lecture, three hours. Enforced requisite: course 202A. Survey of computational methods that are especially useful for statistical analysis. Exploration of computing in C as well as statistical package R. Topics include simulation, smoothing, regression, and principal component analysis. In-depth analysis of particular geometric computing problem with image processing applications, namely construction and inversion of planar tessellations. S/U or letter grading.

202C. Markov Chain Monte Carlo and Optimization. (4)  Lecture, three hours. Requisite: course 202B. Description of Markov chain Monte Carlo (MCMC) sampling techniques, with emphasis on optimization and statistical estimation. Topics include Gibbs samplers, Metropolois/Hastins importance sampling, and simulated annealing. Alternative optimization techniques, including Newton/Raphson, dynamic programming, belief propagation, and variational methods. S/U or letter grading.

204. Nonparametric Function Estimation and Modeling. (4)  Lecture, three hours. Requisite: course 200A. Introduction to many useful nonparametric techniques such as nonparametric density estimation, nonparametric regression, and high-dimensional statistical modeling. Some semiparametric techniques and functional data analysis. Letter grading.

M211. Analysis of Data with Qualitative and Limited Dependent Variables. (4)  (Same as Sociology M242.) Lecture, three hours. Requisites: courses 100A, 100B, and 100C, or Sociology 210A and 210B. Models for binary, polytomous, and ordered outcomes; censored and truncated dependent variables; sample selection bias and qualitative response models; count outcomes; multilevel models; log-linear models. S/U or letter grading.

212. Program Evaluation and Policy Analysis. (4)  Lecture, three hours. Requisite: course 120B. Primary focus on methods of program evaluation. Randomized experiments, observational studies, and topics such as matching, stratification, covariance adjustments, and sensitivity analyses. Letter grading.

M213. Applied Event History Analysis. (4)  (Same as Sociology M213B.) Lecture, three hours. Preparation: exposure to binary response models. Requisites: Sociology 210A, 210B. Introduction to regression-like analyses in which outcome is "time to event." Topics include logit models for discrete-time event history models; piecewise exponential hazards models; proportional hazards; nonproportional hazards; parametric survival models; heterogeneity; multilevel survival models. S/U or letter grading.

216. High-Dimensional Data Analysis. (4)  Lecture, three hours. Requisites: courses 100A, 100B, 100C. Designed for graduate students. Discussion of several statistical methodologies useful for exploring voluminous data, including principle component analysis, clustering and classification, tree-structured analysis, neural network, hidden Markov models, sliced inverse regression (SIR), and principal Hessian direction (PHD). S/U or letter grading.

218. Generalized Linear Models. (4)  Lecture, three hours. Requisite: course 100C or 120A. Nonlinear models, estimation, diagnostics, statistical inference. Applications to models defined by systems of differential equations and robust regression. Introduction to generalized linear models and categorical data analysis. S/U grading.

M221. Time-Series Analysis. (4)  (Formerly numbered 221.) (Same as Earth and Space Sciences M204.) Lecture, three hours. Designed for graduate students. Exploration of methods for analyzing numerical time-series data. Basic topics in temporal and frequency analysis, followed by more recent topics. Examples in various fields including economics, signal processing, and atmospheric sciences. S/U or letter grading.

M222. Spatial Statistics. (4)  (Same as Geography M272 and Urban Planning M215.) Lecture, three hours. Designed for graduate students. Survey of modern methods used in analysis of spatial data. Implementation of various techniques using real data sets from diverse fields, including neuroimaging, geography, seismology, demography, and environmental sciences. S/U or letter grading.

C225. Experimental Design. (4)  Lecture, three hours. Requisite: course 100C or 101B or 110B. Basic principles, analysis of variance, randomized block designs, Latin squares, balanced incomplete block designs, factorial designs, fractional factorial designs, minimum aberration designs, robust parameter designs. Concurrently scheduled with course C151. S/U or letter grading.

C226. Bootstrap, Jackknife, and Resampling Methods. (4)  Lecture, three hours; discussion, one hour. Designed for graduate students. Simple intuitive introduction to practical application of statistics for experiments and surveys in business and biological, medical, physical, and social sciences. Resampling methods -- bootstrap and permutation test -- are table-free and distribution-free, require common sense (not calculus), yet have a broader range of applications than classical parametric statistical procedures. Concurrently scheduled with course C152. S/U or letter grading.

M230. Statistical Computing. (4)  (Same as Biomathematics M280 and Biostatistics M280.) Lecture, three hours. Requisites: course 100C, Mathematics 115A. Introduction to theory and design of statistical programs: computing methods for linear and nonlinear regression, dealing with constraints, robust estimation, and general maximum likelihood methods. Letter grading.

M231. Pattern Recognition and Machine Learning. (4)  (Same as Computer Science M276A.) Lecture, three hours. Designed for graduate students. Fundamental concepts, theories, and algorithms for pattern recognition and machine learning that are used in computer vision, image processing, speech recognition, data mining, statistics, and computational biology. Topics include Bayesian decision theory, parametric and nonparametric learning, clustering, complexity (VC-dimension, MDL, AIC), PCA/ICA/TCA, MDS, SVM, boosting. S/U or letter grading.

232A. Statistical Modeling and Learning in Vision and Science. (4)  Lecture, three hours. Preparation: basic statistics, linear algebra (matrix analysis), computer vision. Computer vision and pattern recognition. Study of four types of statistical models for modeling visual patterns: descriptive, causal Markov, generative (hidden Markov), and discriminative. Comparison of principles and algorithms for these models; presentation of unifying picture. Introduction of minimax entropy and EM-type and stochastic algorithms for learning. S/U or letter grading.

232B. Statistical Computing and Inference in Vision and Image Science. (4)  Lecture, three hours. Preparation: basic statistics, linear algebra (matrix analysis), computer vision. Introduction to broad range of algorithms for statistical inference and learning that could be used in vision, pattern recognition, speech, bioinformatics, data mining. Topics include Markov chain Monte Carlo computing, sequential Monte Carlo methods, belief propagation, partial differential equations. S/U or letter grading.

233. Statistical Methods in Biomedical Imaging. (4)  Lecture, three hours. Requisite: course 100A. Brief review of common general statistical techniques. Advanced statistical methods for analysis of medical imaging, integration, visualization, interrogation, and interpretation of imaging and nonimaging metadata. S/U or letter grading.

234. Statistics and Information Theory. (4)  Lecture, three hours. Preparation: introductory probability theory course. While data compression and transmission are fundamental problems in information theory, field provides insights into fundamentally statistical problems of estimation, prediction, and model selection. Even new concepts of randomness emerge from this line of research. S/U or letter grading.

C235. Data Management. (4)  (Formerly numbered 235.) Lecture, three hours. Requisite: one course from 10, 11, 12, 13, or 14. Proper methods by which researchers should create, document, maintain, and utilize statistical databases. Basics of raw data formats to completion of data archive. Concurrently scheduled with course C156. S/U or letter grading.

C236. Introduction to Bayesian Statistics. (4)  Lecture, three hours; discussion, one hour. Designed for graduate students. Introduction to statistical inference based on use of Bayes theorem, covering foundational aspects, current applications, and computational issues. Topics include Stein paradox, nonparametric Bayes, and statistical learning. Examples of applications include protein alignment algorithms and image denoising procedures. May not be applied toward Ph.D. in Statistics. Concurrently scheduled with course C180. S/U or letter grading.

M237. Data and Media Arts. (4)  (Same as Design | Media Arts M259.) Studio, six hours. Through expanding reach of telecommunications networks and general advancement of data collection technologies, almost every aspect of our lives can be "rendered" in data. Contemplation of use of data in creation of media art and examination of each step in process of data collection, analysis, and representation. Topics include databases and data warehousing, exploratory analysis and visualization, clustering and pattern finding, sampling, and various data mining algorithms. Exploration, through discussions, of fundamental concepts like complexity and randomness. Techniques that organize data, search for patterns, and create meaningful and/or expressive representations. Letter grading.

238. Vision as Bayesian Inference. (4)  Lecture, three hours. Requisite: course 100A or 200A. Formulation of vision as Bayesian inference using models developed for designing artificial vision systems. Applied to statistics, they define ideal observer models that can be used to model human performance and serve a benchmark. S/U or letter grading.

240. Multivariate Analysis. (4)  Lecture, three hours. Requisite: course 200B. Distributions in several dimensions, partial and multiple correlation. Normal distribution theory, Wishart distribution, Hotelling T². Principal components, canonical correlation, discriminant analysis. Introduction to linear structural relations and factor analysis. Letter grading.

M241. Causal Inference. (4)  (Same as Computer Science M262C.) Lecture, four hours. Requisite: Computer Science 112 or equivalent probability theory course. Techniques of using computers to interpret, summarize, and form theories of empirical observations. Mathematical analysis of trade-offs between computational complexity, storage requirements, and precision of computerized models. Letter grading.

M242. Multivariate Analysis with Latent Variables. (4)  (Same as Political Science M208D and Psychology M257.) Lecture, three hours. Introduction to models and methods for analysis of data hypothesized to be generated by unmeasured latent variables, including latent variable analogues of traditional methods in multivariate analysis. Causal modeling: theory testing via analysis of moment structures. Measurement models such as confirmatory, higher-order, and structured-means factory analytic models. Structural equation models, including path and simultaneous equation models. Parameter estimation, hypothesis testing, and other statistical issues. Computer implementation. Applications. S/U or letter grading.

M243. Logic, Causation, and Probability. (4)  (Same as Epidemiology M204.) Lecture, four hours. Preparation: two terms of statistics or probability and statistics. Recommended requisite: Epidemiology 200C. Principles of deductive logic and causal logic using counterfactuals. Principles of probability logic and probabilistic induction. Causal probability logic using directed acyclic graphs. S/U or letter grading.

M244. Statistical Analysis with Latent Variables. (4)  (Same as Education M231E.) Lecture, three hours. Requisites: Education 231A, 231B. Extends path analysis (causal modeling) by considering models with measurement errors and multiple indicators of latent variables. Confirmatory factor analysis, covariance structure modeling, and multiple-group analysis. Identification, estimation, testing, and model building considerations. Letter grading.

M245. History of Statistics. (4)  (Same as History M296.) Seminar, three hours. History of statistics ranges over vast and diverse territory. Development of mathematical methods; philosophical, political, and social issues that were linked to their emergence and use. S/U or letter grading.

M250. Statistical Methods for Epidemiology. (4)  (Same as Biostatistics M211 and Epidemiology M211.) Lecture, four hours. Preparation: two terms of statistics (such as Biostatistics 100A, 100B). Requisites: Epidemiology 200B, 200C. Concepts and methods tailored for analysis of epidemiologic data, with emphasis on tabular and graphical techniques. Expansion of topics introduced in Epidemiology 200B and 200C and introduction of new topics, including principles of epidemiologic analysis, trend analysis, smoothing and sensitivity analysis. S/U or letter grading.

M251. Statistical Methods for Life Sciences. (4)  (Same as Ecology and Evolutionary Biology M216.) Lecture, three hours. Requisite: course 13. Fundamentals of statistics as applied in life sciences, including statistical inferences for continuous and categorical data (estimation, testing of means and proportions, ANOVA) study design, linear regression, and introduction to principle components analysis. Methods to be implemented on computer with SAS. S/U or letter grading.

CM252. Statistical Methods for Physical Sciences. (4)  (Same as Atmospheric and Oceanic Sciences CM213.) Lecture, three hours. Designed for graduate students. Statistical framework for data analysis in fields of atmospheric sciences, astronomy, geology, and chemistry, depending on class composition. Presentation of popular techniques in all fields, with emphasis on applications and data, not theory, although some understanding of theory is needed. Concurrently scheduled with course CM185. S/U or letter grading.

253. Statistical Methods for Ecology and Population Biology. (4)  Lecture, three hours. Designed for graduate students. Conceptual underpinnings of modern applied statistical analysis to prepare students to think critically about data and statistical models used in biological sciences. S/U or letter grading.

M254. Statistical Methods in Computational Biology. (4)  (Same as Biomathematics M271.) Lecture, three hours; discussion, one hour. Preparation: elementary probability concepts. Requisite: course 100A. Training in probability and statistics for students interested in pursuing research in computational biology, genomics, and bioinformatics. Letter grading.

CM255. Introduction to Statistical Analysis of Environmental Data. (4)  (Same as Environmental Science and Engineering M255.) Lecture, three hours. Designed for graduate students. Routine intermediate applied statistics course, with emphasis on applications to environmental data and statistical computing with the language R. Statistical analysis and scientific report from real data required. Concurrently scheduled with course C155. S/U or letter grading.

257. Design, Analysis, and Modeling for Embedded Sensing. (4)  Lecture, three hours; discussion, one hour. Recommended preparation: knowledge of probability and regression analysis. Limited to graduate students. Analysis of data produced by embedded sensing, which is product of several technological advances such as low-power computing and communications platforms, and robot devices. S/U or letter grading.

C260. Site-Specifics Topics. (4)  Seminar, three hours. Tracking of invisible flows of data through greater Los Angeles metropolitan area, with focus on small number of specific sites situated prominently in both physical and virtual (data) spaces. Documentation of kinds of data that originate, terminate, or simply route through each location. Consideration of analyses (visual, computational, or simply informal), decisions that are made, and actions that are taken on basis of these data, whether they be human or automated responses. Documentation of how patterns of data acquisition and analysis dictate behaviors, enable or restrict movements, and shape local community. Alterations or additions to data flows that could improve quality of life for inhabitants of or visitors to sites. May be repeated for credit; however, only one C260 may be applied toward any graduate degree. Concurrently scheduled with course C160. S/U or letter grading.

C283. Statistical Models in Finance. (4)  Lecture, three hours. Requisite: course 100B or 110B. Designed for graduate students. Statistical techniques in investment theory using real market data. Portfolio management, risk diversification, efficient frontier, single index model, capital asset pricing model (CAPM), beta of a stock, European and American options (Black/Scholes model, binomial model). Concurrently scheduled with course C183. S/U or letter grading.

285. Seminar: Computing for Statistics. (2 to 4)  Seminar, one to three hours. Topics in various statistical areas by means of lectures and informal conferences with staff members. S/U grading.

M286. Seminar: Statistical Problem Solving for Population Biology. (2)  (Same as Ecology and Evolutionary Biology M286.) Seminar, two hours. Designed for graduate students. Statistical solutions to complex data analysis and/or experimental design problems encountered by biology graduate students in their own research. S/U or letter grading.

287. Seminar: Gene Expression and Systems Biology. (2)  Seminar, two hours. Designed for graduate students (open to undergraduate students with consent of instructor). With high-throughput technologies such as genomic sequencing, microarray gene expressions, Chromatin-ImmunoPrecipitation DNA chip (ChIP-chip), and mass spectrometry (MS/MS) proteomics, scientists are collecting genetic, genomic, and pathway data at rates far beyond imagination one decade ago. Such gigantic volumes of data produced cannot be analyzed and understood without highly sophisticated computational methods guided by mathematical and statistical principles. Cutting-edge genomics research from statistical data analytic point of view. S/U or letter grading.

290. Current Literature in Statistics. (2)  Seminar, one hour. Topics in various statistical areas by means of lectures and informal conferences with staff members. S/U grading.

291. Statistics Consulting Seminar. (4)  Seminar, three hours. Preparation: at least one UCLA graduate-level statistics course. Exposure to realistic statistical and scientific problems that appear in typical interactions between statisticians and researchers, with lectures centered on case studies presented by faculty members and invited speakers from business and academic fields. Applied regression analysis and design of experiments, together with basic statistical programs. Presentations and written reports required. S/U or letter grading.

292. Graduate Student Statistical Packages Seminar. (1 to 2)  Seminar, two hours. Introduction to various statistical packages. How to handle data in different packages (input, output, data management, treatment of missing data), general syntax of different programming languages, and good practice for writing own statistical functions. S/U grading.

293. Graduate Student Research Seminar. (2)  Seminar, two hours. Designed for graduate statistics students. Participating seminar in which various aspects of performing research are discussed by variety of faculty members. Exposure to current research topics with statistical implications to help students select possible thesis or dissertation topics. May not be applied toward degree course requirements. S/U grading.

C294. Scientific Writing. (2)  Seminar, one hour. Development of oral and written presentations of statistical data. Objectives and techniques of scientific writing and practice with different forms of professional writing. Participation in oral presentations of student work. Concurrently scheduled with course C184. S/U or letter grading.

C295. Fundamentals of Scientific Writing. (2)  Seminar, one hour. Development and perfection of student written communication skills through variety of scientific writing and reading assignments. Objectives and techniques of scientific writing and practice with different forms of professional writing. Analysis of quality of writing, including control, clarity, grammar, and mechanics. Concurrently scheduled with course C182. S/U or letter grading.

296. Participating Seminar: Statistics. (1 to 2)  Seminar and discussion by staff and students. S/U grading.

370. Teaching of Statistics. (4)  Lecture, four hours. Exhaustive review of literature in teaching of statistics followed by analysis of what is missing in this area. Discussion of prevalent education, cognitive psychology, and evaluation theories and strategies that help to improve teaching of statistics. Letter grading.

375. Teaching Apprentice Practicum. (1 to 4)  Seminar, to be arranged. Preparation: apprentice personnel employment as teaching assistant, associate, or fellow. Teaching apprenticeship under active guidance and supervision of regular faculty member responsible for curriculum and instruction at UCLA. May be repeated for credit. S/U grading.

495A. Teaching College Statistics. (2)  Seminar, two hours; intensive training at beginning of Fall Quarter. Required of all potential departmental teaching assistants and new Ph.D. students. Practical and theoretical issues in teaching of statistics. S/U grading.

495B. Teaching College Statistics. (2)  Seminar, two hours. Weekly discussion and intensive training for all first-year teaching assistants that addresses practical and theoretical issues in using technology to teach statistics, including use of statistical software as education tool. S/U grading.

495C. Evaluation of Teaching Assistants. (2)  Seminar, two hours. Overview of new trends and directions in teaching of statistics. Observation of teaching assistants twice by instructor to give them chance to observe and analyze their own strengths and weaknesses and think about how they can improve their teaching. S/U grading.

596. Directed Individual Study or Research. (2 to 8)  Tutorial, to be arranged. Supervised individual reading and study on project approved by a faculty member. May be repeated for credit. Letter grading.

598. M.S. Thesis Research. (2 to 12)  Tutorial, to be arranged. Designed for second-year statistics M.S. students. Study and research for M.S. thesis. May be repeated for credit. S/U grading.

599. Ph.D. Dissertation Research. (2 to 12)  Tutorial, to be arranged. Preparation: advancement to Ph.D. candidacy. Study and research for Ph.D. dissertation. May be repeated for credit. S/U grading.