Stat Computing

Applied Statistics Courses Offered at UCLA

In our statistical consulting we often find that researchers are not aware of the fantastic array of applied statistics courses that are offered around the UCLA campus. We would like to help connect students and researchers with applied statistics courses so they can take advantage of the wealth of applied statistics knowledge from all corners of the campus. This web page is our attempt to help show the terrific set of applied statistics courses that are available on campus and to help connect students and researchers with these courses. 

This listing is certainly not comprehensive and no slight is intended if your course is omitted -- in fact, please email us at to let us know about additional courses (past, present or future) that should be listed here.

Spring 2008

• Biomathemathics
  • Biomath 204: Biomedical Data Analysis, Professor Suchard.
    Quantity and quality of observations have been greatly affected by present-day extensive use of computers. Problem-oriented study of latest methods in statistical data analysis and use of such arising in laboratory and clinical research.
  • Biomath M260A: Methodology in Clinical Research I, Staff
    Presentation of principles and practices of major disciplines underlying clinical research methodology, such as biostatistics, epidemiology, pharmacokinetics.
  • Biomath 265B: Data Analysis Strategies, Staff
    Continuation of course 265A; use of SAS computer language.
  • Biomath 266: Advanced Biostatistics, Professor Elashoff and Professor Tseng.
    Some traditional multivariate methods, such as principle components, factor analysis, cluster analysis, and more contemporary methods, including recursive partitioning and missing data. Multilevel and longitudinal analysis.
   
• Biostatistics
  • Biostat 100A: Introduction to Biostatistics, Professor Lee.
    Introduction to methods and concepts of statistical analysis. Sampling situations, with special attention to those occurring in biological sciences. Topics include distributions, tests of hypotheses, estimation, types of error, significance and confidence levels, sample size.
  • Biostat 115: Topics in Estimation, Professor Li.
    Small and large sample properties of common estimation techniques arising in biostatistical application.
  • Biostat 200C: Biostatistics, Professor Wong.
    Measures of association and analysis of categorical data, theory of generalized linear models.
  • Biostat 213: Statistical Simulation Techniques, Professor Kitchen.
    Techniques for simulating important statistical distributions, with applications in biostatistics.
  • Biostat M220: Advanced Experimental Statistics, Professor Garfinkel.
    Introduction to statistics with focus on computer simulation instead of formulas. Bootstrap and Monte Carlo methods used to analyze physiological data.
  • Biostat 233: Statistical Methods in AIDS, Professor Cumberland.
    Coverage of methods necessary to address statistical problems in AIDS research, including projection methods for size of AIDS epidemic and methods for estimating incubation distribution.
  • Biostat M235: Causal Inference, Professor Belin.
    Selection bias, confounding, ecological paradox, contributions of Fisher and Neyman. Rubin model for causal inference, propensity scores. Analysis of clinical trials with noncompliance. Addressing confounding in longitudinal studies. Path analysis, structural equation, and graphical models. Decision making when causality is disputed.
  • Biostat 251: Multivariate Biostatistics, Professor Weiss.
    Multivariate analysis as used in biological and medical situations. Topics from multivariate distributions, component analysis, factor analysis, discriminant analysis, MANOVA, MANCOVA, longitudinal models with random coefficients.
  • Biostat 406: Applied Multivariate Biostatistics, Professor Afifi.
    Use of multiple regression, principal components, factor analysis, discriminant function analysis, logistic regression, and canonical correlation in biomedical data analysis.
   
• Economics
  • Econ 41: Statistics for Economists, Professor Hahn.
    Introduction to probability and statistics for economists, with emphasis on rigorous arguments.
  • Econ 103: Introduction to Econometrics, Professor Ackerberg.
    Introduction to theory and practice of econometrics, with goal to make students effective consumers and producers of empirical research in economics. Emphasis on intuitive understanding rather than on rigorous arguments; concepts illustrated with applications in economics.
  • Econ 203C: Systems Models, Professor Buchinsky.
    Multivariate regression, simultaneous equation estimation, identification, and latent variables.
  • Econ 232B: Topics in Econometrics: Time Series: Statistics for Economists, Professor Hahn.
    Stationary stochastic processes, Box/Jenkins methods, spectral analysis, forecasting, rational expectation models, analysis of macroeconomic data.
   
• Education
  • Educ 221: Computer Analyses of Empirical Data in Education, Professor Sax.
    Designed to develop conceptual and technical skills needed for designing and executing empirical research utilizing statistical packages. Each student conducts two original studies. Equal emphasis on techniques of data analysis and interpretation of results.
  • Educ 222C: Qualitative Data Reduction and Analysis, Professor Erickson.
    Continuation of fieldwork project started in course 222B, with focus on practical skills and conceptual/methodological issues involved in reducing and analyzing qualitative data.
  • Educ 230A: Introduction to Research Design and Statistics, Professor Schlackman.
    Key concepts and issues in design and conduct of social sciences research. Introduction to descriptive statistics and fundamentals of statistical inference.
  • Educ 230C: Linear Statistical Models in Social Science Research: Analysis of Designed Experiments, Professor Martinez-Fernandez and Professor Hser.
    Solid and comprehensive training in experimental design and analysis methods, especially use of analysis of variance methods.
   
• Epidemiology
  • Epidem 200C: Methods III: Analysis, Professor Greenland.
    Introduction to basic concepts, principles, and methods of epidemiologic data analysis.
  • Epidem 244: Research Methods in Cancer Epidemiology, Professor Zhang.
    Biologic, quantitative, philosophical, and administrative considerations in epidemiologic cancer research. Hypothesis specification and choice of study design. Uses of descriptive epidemiology, cohort studies, case control studies. Clustering, screening, and cancer control. Means of identifying subjects and controls. Design of instruments.
  • Epidem 265: Epidemiology Methods in Occupational and Environmental Health, Professor Kheifets.
    Introduction to epidemiology methods applied to evaluation of human health consequences of occupational and environmental hazards, including study design, exposure assessment, and statistical techniques commonly encountered in research focused on assessing adverse health effects resulting from occupational and environmental exposures. Topics include clusters, meta-analysis, risk assessment, and policy development. Illustrated by case studies, with focus on techniques to critically evaluate and interpret current literature.
  • Epidem M403: Computer Management and Analysis of Health Data Using SAS, Professor Smith.
    Introduction to practical issues in management and analysis of health data using SAS programming language. Cross-sectional and longitudinal population-based data sets to be used throughout to illustrate principles of data management and analysis for addressing biomedical and health-related hypotheses.
  • Epidem M418: Rapid Epidemiologic Surveys in Developing Countries, Professor Frerichs.
    Introduction to practical issues in management and analysis of health data using SAS programming language. Cross-sectional and longitudinal population-based data sets to be used throughout to illustrate principles of data management and analysis for addressing biomedical and health-related hypotheses.
   
• Health Services
  • Hlt Ser 237C: Issues in Health Services Methodologies, Professor Needleman.
    Intended to train students in statistical and economic methods used in health services research, with focus on practical application of advanced regression models.
   
• Management
  • Mgmt 242A: Models for Operations Planning, Scheduling, and Control, Professor Rajaram.
    Survey of modeling approaches to managerial planning and decisions. Emphasis on ability to recognize situations where models can be used advantageously, to work effectively with model building specialists, and to make good use of models once they have been developed.
  • Mgmt 269C: Quantitative Research in Marketing, Professor Thomadsen.
    Students are assumed to have good background in marketing principles and to be familiar with probability, statistics, mathematical programming, and econometrics. Review of a range of quantitative models as applied in marketing research.
  • Mgmt 465A: Quantitative Research for Managers, Professor Mamer.
    Survey of modeling approaches to managerial planning and decisions. Emphasis on ability to recognize situations where models can be used advantageously, to work effectively with model building specialists, and to make good use of models once they have been developed.
  • Mgmt 468: Macroeconomics and Economic Forecasting, Professor Leamer.
    Macroeconomic theory and its application to business forecasting. Major economic indicators and their historical description of the U.S. economy; theoretical tools that business economists use to analyze impacts of monetary and fiscal policy; macroeconometric techniques applicable to business decisions.
   
• Political Science
  • Pol Sci 6: Introduction to Data Analysis, Professor Honaker.
    Introduction to collection and analysis of political data, with emphasis on application of statistical reasoning to study of relationships among political variables. Use of computer as aid in analyzing data from various fields of political science, among them comparative politics, international relations, American politics, and public administration.
  • Pol Sci 200C: Statistical Methods III, Professor Denardo.
    Statistical modeling of political processes. Topics include simultaneous equations models, discrete choice models, time-series models.
  • Pol Sci 200E: Advanced Topics in Quantitative Methods, Lewis.
    Topics vary each year and have included instrumental variables principal components and scaling, models of selection, models of duration, ecological inference, and hierarchal models. Student-led presentations on relevant statistical theory and applications. Monte Carlo simulations and replications of well-known studies used to demonstrate how various models work and how they are applied in practice.
  • Pol Sci 209: Special Topics in Formal Theory and Quantitative Methods, Professor Groseclose.
    Study of nexis of formal theory and statistical methods. Students read set of papers, most of which construct theoretical model to examine political or economic phenomenon. Papers structurally estimate parameters in theoretical model.
   
• Psychology
  • Psych 100A: Psychological Statistics, Professor Shams.
    Basic statistical procedures and their application to research and practice in various areas of psychology.
  • Psych 100B: Research Methods in Psychology, Professor Firstenberg.
    Introduction to research methods and critical analysis in psychology. Lecture and laboratory topics include experimental and nonexperimental research methods, statistical design and analysis as applied to a broad range of basic and applied research issues.
  • Psych 255A: Quantitative Aspects of Assessment, Professor Reise.
    Introduction to issues concerning empirical measurement of abstract constructs using both classical and modern empirical techniques. Hands-on approach allows students to develop practical experience. In addition to discussion of issues concerning reliability and validity, topics include exposure to analytic approaches, including item response theory, multiple regression, principal components analysis, exploratory factor analysis, confirmatory factor analysis, path analysis, and structural equation modeling.
  • Psych 256: Advanced Regression Analysis, Professor Krull.
    Advanced treatment of traditional topics -- model comparison strategies, evaluation of model assumptions, outliers, mediation, moderation, categorical variable, polynomials, transformations, logistic regression.
  • Psych 258: Special Problems in Psychological Statistics, Professor Nandy.
    Theory and practice of measuring neural activity in human brain using functional magnetic resonance imaging. Covers motivation for using fMRI, BOLD response, experimental design, data acquisition, pre-processing of fMRI data, basic statistics, introduction to SPM, temporal and spatial modeling of data, group modeling of data, and advanced designs.
   
• Public Policy
  • Pub Plc M224B: Advanced Geographic Information Systems, Professor Ong and Professor Smart.
    Principles and skills of geographic analysis and modeling; managing, processing, and interpreting spatial data. Especially useful for students interested in environmental, demographic, suitability, and transportation-related research. Scripts (Avenue), modeling (Spatial Analyst), network analysis, and transportation modeling (TransCAD).
   
• Sociology
  • Sociol 210C: Intermediate Statistical Methods, Professor Sweeney.
    Survey of advanced statistical methods used in social research, with focus on problems for which classical linear regression model is inappropriate, including categorical data, structural equations, longitudinal data, incomplete and erroneous data, and complex samples.
   
• Statistics
  • Stats 10: Introduction to Statistical Reasoning, Professor Davis and Professor Ioudina.
    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.
  • Stats 13: Introduction to Statistical Methods for Life and Health Sciences, Professor Schoenberg and Professor Ioudina.
    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.
  • Stats 35A: Interactive and Computational Probability, Professor Dinov.
    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.
  • Stats100C: Linear Model with Experimental Design, Professor Sanchez and Professor Xu.
    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.
  • Stats101C: Introduction to Regression and Data Mining, Professor Hansen.
    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.
  • Stats 102C: Markov Chain Monte Carlo Methods: Introduction, Professor Zhou.
    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.
  • Stats110A: Applied Statistics, Professor Wu.
    Probability, distributions, expectation, estimation, central limit theorem, confidence intervals, testing.
  • Stats 110B: Applied Statistics, Professor Esfandiari.
    One- and two-sample problems, goodness of fit and contingency tables, correlation and regression, analysis of variance, nonparametrics.
  • Stats 112: Statistical Methods for the Social Sciences, Professor Esfandiari.
    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.
  • Stats C173: Introduction to Applied Geostatistics, Professor Christou.
    Geostatistics can be applied to many problems in other disciplines such as hydrology, traffic, air and water pollution, epidemiology, economics, geography, waste management, forestry, oceanography, meteorology, and agriculture and, in general, to every problem where data are observed at geographic locations. Acquisition of knowledge from different areas that can be used to analyze real spatial data problems and to connect geostatistics with geographic information systems (GIS).
  • Stats C183: Statistical Models in Finance, Professor Christou.
    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).
  • Stats 201C: Advanced Modeling and Data Mining, Professor Zhou.
    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.
  • Stats 202C: Markov Chain Monte Carlo and Optimization, Professor Sabatti.
    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.
  • Stats 232B: Statistical Computing and Inference in Vision and Image Science, Professor Zhu.
    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.
   
• Urban Planning
  • Urbn Pl M206B: Advanced Geographic Information Systems (Same as Public Policy M224A), Professor Ong and Professor Smart.
    Principles of Geographic Information Systems (GIS) and applied techniques of using spatial data for mapping and analysis. Topics include data quality, data manipulation, spatial analysis, and information systems. Use of mapping and spatial analysis to address a planning problem.

These are classes from past quarters. If you are interested in these courses, you might contact the professor to inquire about the next time the course will be offered. Often times courses are offered the same quarter each year or sometimes in alternating years.

Winter 2008

• Biomathemathics
  • Biomath 171: Regression Analysis in Medical Sciences, Professor Elashoff.
    Proficiency in applied regression analysis, with focus on interpretation of results and performing computation. Primary topics include simple linear regression, multiple regression, regression model selection, analysis of variance, logistic regression, and survival analysis.
  • Biomath M211: Mathematical and Statistical Phylogenetics (Same as Human Genetics M211), Professor Suchard.
    Theoretical models in molecular evolution, with focus on phylogenetic techniques. Topics include evolutionary tree reconstruction methods, studies of viral evolution, phylogeography, and coalescent approaches. Examples from evolutionary biology and medicine.
  • Biomath 265A: Data Analysis Strategies I, Professor Seeman.
    Designed to provide students with hands-on experience developing and testing hypotheses using various types of databases. Topics include developing testable hypothesis, data management, and analysis strategies and written presentation of findings. Experience with full process of hypothesis generation, operationalization of variables, selection of analysis techniques, and presentation of findings so students are better prepared to complete data analysis, interpretation of results, and written presentation of their findings (e.g., for master's thesis and subsequent articles).
  • Biomath M271: Statistical Methods in Computational Biology (Same as Statistics M254), Professor Zhou.
    Training in probability and statistics for students interested in pursuing research in computational biology, genomics, and bioinformatics.
           
   
• Biostatistics
  • Biostat 100B: Introduction to Biostatistics, Professor Sugar.
    Introduction to analysis of variance, linear regression, and correlation analysis.
  • Biostat 110B: Basic Biostatistics, Professor Cumberland.
    Topics include elementary analysis of variance, simple linear regression; topics related to analysis of variance and experimental designs.
  • Biostat 200B: Biostatistics, Professor Belin.
    Multiple linear regression, including model validation, influence of observations, regression diagnostics; discriminant analysis; principal components; factor analysis and clinical trials.
  • Biostat 201: Topics in Applied Regression, Professor Gornbein.
    Further studies in multiple linear regression, including applied multiple regression models, regression diagnostics and model assessment, factorial and repeated measure analysis of variance models, nonlinear regression, logistic regression, propensity scores, matching versus stratification, Poisson regression, and classification trees. Applications to biomedical and public health scientific problems.
  • Biostat M236: Analysis of Repeated Measures Designs (Same as Biomathematics M282), Professor Weiss.
    Presentation of classical and modern theories for analysis of repeated measures designs, with focus on computation and robustness.
  • Biostat 250B: Linear Statistical Models, Professor Horvath.
    Topics include linear algebra applied to linear statistical models, distribution of quadratic forms, Gauss/Markov theorem, fixed and random component models, balanced and unbalanced designs.
  • Biostat 255: Advanced Topics and Probability in Biostatistics, Professor Dabrowska.
    Topics include conditioning, modes of convergence, basic limit results for empirical processes, von-Mises calculus, and notions of efficiency in statistics. Applications cover M-L-R estimation in two-sample and regression models, goodness of fit methods, smoothing techniques, and bootstrap.
  • Biostat M278: Statistical Analysis of DNA Microarray Data, Professors Elashoff and Horvath.
    Instruction in use of statistical tools used to analyze microarray data. Structure corresponds to analytical protocol investigators might follow when working with microarray data.
  • Biostat 288: Seminar: Statistics in AIDS, Professor Cumberland.
    Recent statistical developments in analysis of AIDS data. Participants or outside speakers present their own research or discuss articles from literature.
  • Biostat M403B: Computer Management and Analysis of Health Data Using SAS, STAFF.
    Recent statistical developments in analysis of AIDS data. Participants or outside speakers present their own research or discuss articles from literature.
  • Biostat 414: Principles of Sampling, Professor Lee.
    Statistical aspects of design and implementation of sample survey. Techniques for analysis of data, including estimates and standard errors. Avoiding improper use of survey data.
  
   
• Economics
  • Econ 41: Statistics for Economists, Kyriazidou.
    Introduction to probability and statistics for economists, with emphasis on rigorous arguments.
  • Econ 103: Introduction to Econometrics, Professor Black.
    Introduction to theory and practice of econometrics, with goal to make students effective consumers and producers of empirical research in economics. Emphasis on intuitive understanding rather than on rigorous arguments; concepts illustrated with applications in economics.
  • Econ 203B: Introduction to Econometrics: Single Equation Models, Professor Kyriazidou.
    Estimation of basic linear regression model, testing hypotheses, generalized least squares, serial correlation, heteroskedasticity, multicollinearity, error-in-variables, distributed lags, qualitative dependent variables, and forecasting.
  
   
• Education
  • Educ 209C: Research and Evaluation in Higher Education, Professor Sax.
    Development of conceptual and practical understanding of research and evaluation in higher education. Topics include basic statistics, survey design, data analysis, assessment issues, and research proposal writing.
  • Educ 211A: Measurement in Education: Underlying Theory, Professor Martinez-Fernandez.
    Measurement theory as applied to testing, with focus primarily on classical test theory; implications of theories for test construction and selection; current status of validity and reliability theory.
  • Educ 222B: Participant-observation Field Methods, Professor Gutierrez.
    First of two courses on participant-observation field methods. Key skills (e.g., observation, recording, interviewing, role management, data storage) learned through classroom lectures and simulations, and by conducting actual field-based research project.
  • Educ 230B: Multiple Regression Analysis, Professor Webb.
    Solid and comprehensive training in regression-based methods for analyzing quantitative social science data.
  
   
• Epidemiology
  • Epidem 200B: Methods II: Prediction and Validity, Professors Olsen and Ritz.
    Introduction to basic concepts, principles, and methods of chronic and infectious disease epidemiology.
  • Epidem M403: Computer Management and Analysis of Health Data Using SAS (Same as Biostatistics M403B), Professor Smith.
    Introduction to practical issues in management and analysis of health data using SAS programming language. Cross-sectional and longitudinal population-based data sets to be used throughout to illustrate principles of data management and analysis for addressing biomedical and health-related hypotheses.
  
   
• Health Services
  • Hlt Ser 237B: Special Topics in Health Services: Research Methodology, Professor Ponce.
    Introduction to multivariate analysis techniques in health services research. Model specification and estimation, regression diagnostics, variable transformations, instrumental variables. Application of statistical software using large-scale national database.
  • Hlt Ser 249F:  Special Topics in Health Services: Quality Assessment and Assurance, Professor Brook.
    Fundamental issues in quality assessment, quality assurance, and measurement of health status.
  
   
• Management
  • Mgmt 213B: Statistical Methods in Management, Professor Stern.
    Introduction to parameter and interval estimation, simple and multiple linear regression and correlation, fixed, random, and mixed effects analysis of variance models and nonparametric statistics, all as they apply to management studies.
  
   
• Nursing
  • Nursing 204: Research Design and Critique, Professor Giger.
    Complex research designs and analysis of multiple variables, and research utilization. Emphasis on techniques for control of variables, data analysis, and interpretation of results. Analysis in depth of interrelationship of theoretical frameworks, design, sample selection, data collection instruments, and data analysis techniques. Content discussed in terms of clinical nursing research problems and how these apply to clinical settings.
  
   
• Political Science
  • Pol Sci 6: Introduction to Data Analysis, Professor Denardo.
    Introduction to collection and analysis of political data, with emphasis on application of statistical reasoning to study of relationships among political variables. Use of computer as aid in analyzing data from various fields of political science, among them comparative politics, international relations, American politics, and public administration.
  • Pol Sci 200B: Statistical Methods II, Professor Lewis.
    Applications of multiple regression in political science.
  • Pol Sci 209: Special Topics in Formal Theory and Quantitative Methods, Professor Chwe.
    Survey of mathematical models of voting and other forms of political participation, focusing on recent approaches which try to incorporate psychological considerations.
  
   
• Psychology
  • Psych 100A: Psychological Statistics, Professor Nandy.
    Basic statistical procedures and their application to research and practice in various areas of psychology.
  • Psych 100B: Research Methods in Psychology, Professor Firstenberg.
    Introduction to research methods and critical analysis in psychology. Lecture and laboratory topics include experimental and nonexperimental research methods, statistical design and analysis as applied to a broad range of basic and applied research issues.
  • Psych 136D:  Research Methods in Health Psychology, Professor Bower.
    Research methods used in health psychology, including experimental, quasi-experimental, and nonexperimental methods. Examples and projects from health psychology.
  • Psych 220B:  Research Methods in Social Psychology, Professor Huo.
    Research design and methodological issues in experimental and nonexperimental social research.
  • Psych 250B: Advanced Psychological Statistics, Professors Reise and Nandy.
    Advanced experimental design and planning of investigations.
  
   
• Public Policy
  • Pub Plc 208: Statistical Methods of Policy Analysis II, Professors Drennan and Elias.
    Quantitative studies of public policy, covering regression analysis and its application to public policy questions.
     
• Social Welfare
  • Soc Wlf 285H:  Program Evaluation Research, Professor Moon.
    Discussion of differences and similarities between evaluation and other research, alternative program evaluation methods, roles and limitations of evaluation research in real world, development of proposals for feasible program evaluation research.

• Sociology
  • Sociol 112: Introduction to Mathematical Sociology, Professor McFarland.
    Mathematical treatment of several sociological phenomena, such as occupational mobility, population growth, organizational structure, and friendship patterns, each covered in some detail, including initial development and subsequent evaluation and modification (emphasizing both deductive and computational aspects of mathematics).
  • Sociol 210B: Intermediate Statistical Methods II, Professor Campbell.
    Intermediate statistical methods using computers: probability theory, sampling distributions, hypothesis testing, interval estimation, multiple regression and correlation, experimental design, analysis of variance and covariance, contingency tables, sampling theory.
  • Sociol 212B: Survey Data Analysis, Professor Treiman.
    Analysis and interpretation of primarily nonexperimental quantitative data, with focus on sample survey and census data. Extensive practice at utilizing statistical methods encountered in previous courses, culminating in term paper in style of "American Sociological Review" or similar journal article. Topics include simple tabular analysis, log-linear analysis, ordinary least squares regression, robust regression, binomial and multinomial logistic regression, and scale construction. Logic of analysis and problems of statistical inference, including diagnostic procedures and methods for handling complex sample survey designs.
  
   
• Statistics
  • Stats 105: Statistics for Engineers, Professor Xu.
    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.
  • Stats 110A: Applied Statistics, Professor Ioudina.
    Probability, distributions, expectation, estimation, central limit theorem, confidence intervals, testing.
  • Stats 112: Statistical Methods for the Social Sciences, Professor Esfandiari.
    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.
  • Stats 130B: Statistical Analysis with SAS, Professor Lew.
    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.
  • Stats C151: Experimental Design (Concurrently scheduled with Stat C225), Professor Xu.
    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.
  • Stats C160: Site-Specific Topics (Concurrently scheduled with Stat C260), Professor Hansen.
    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.
  • Stats C180: Introduction to Bayesian Statistics (Concurrently scheduled with Stat C236),  Professor Sabatti.
    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.
  • Stats 201B: Regression Analysis: Model Building, Fitting, and Criticism, Professor Paik Schoenberg.
    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.
  • Stats 202B: Numerical Linear Algebra and Random Numbers, Professor DeLeeuw.
    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.
  • Stats M221: Time-Series Analysis, Professor Keilis-Borok.
    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.
  • Stats C225: Experimental Design, Professor Xu.
    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.
  • Stats 232A: Statistical Modeling and Learning in Vision and Science, Professor Zhu.
    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.
  • Stats C236: Introduction to Bayesian Statistics, Professor Sabatti.
    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.
  • Stats 239: Probabilistic Models of Cognition, Professor Yuille.
    Modeling aspects of human cognition, designing artificial intelligence systems. Introduction to conceptual foundations and basic mathematical and computational techniques. Topics illustrated on different aspects of cognition.
  • Stats M254: Statistical Methods in Computational Biology (Same as Biomathematics M271), Professor Zhou.
    Training in probability and statistics for students interested in pursuing research in computational biology, genomics, and bioinformatics.
  
   
• Urban Planning
  • Urbn Pl M206A: Introduction to Geographic Information Systems (Same as Public Policy M224A), Professors Estrada, Simon, and Gaines.
    Principles of Geographic Information Systems (GIS) and applied techniques of using spatial data for mapping and analysis. Topics include data quality, data manipulation, spatial analysis, and information systems. Use of mapping and spatial analysis to address a planning problem.
  • Urbn Pl 220B: Quantitative Analysis in Urban Planning II, Professors Liggett, Rhoads, and Lee.
    Introduction to concepts of statistical inference and modeling, with emphasis on urban planning applications. Topics include sampling, hypothesis testing, analysis of variance, correlation, and simple and multiple regression. Use of computer as tool in statistical analysis and modeling.

Fall 2007

• Biomathematics
  • Biomath M232: Statistical Analysis of Incomplete Data (Same as Biostatistics M232), Staff.
    Discussion of statistical analysis of incomplete data sets, with material from sample survey, econometric, biometric, psychometric, and general statistical literature. Topics include treatment of missing data in statistical packages, missing data in ANOVA and regression imputation, weighting, likelihood-based methods, and nonrandom nonresponse models. Emphasis on application of methods to applied problems, as well as on underlying theory.
  • Biomath M234: Applied Bayesian Inference (Same as Biostatistics M234), Staff.
    Bayesian approach to statistical inference, with emphasis on biomedical applications and concepts rather than mathematical theory. Topics include large sample Bayes inference from likelihoods, noninformative and conjugate priors, empirical Bayes, Bayesian approaches to linear and nonlinear regression, model selection, Bayesian hypothesis testing, and numerical methods.
  • Biomath M281: Survival Analysis (Same as Biostatistics M215), Professor Li.
    Statistical methods for analysis of survival data.

   

• Biostatistics
  • Biostat 100A: Introduction to Biostatistics, Professor Gjertson.
    Introduction to methods and concepts of statistical analysis. Sampling situations, with special attention to those occurring in biological sciences. Topics include distributions, tests of hypotheses, estimation, types of error, significance and confidence levels, sample size.
  • Biostat 110A: Basic Biostatistics, Professor Crespi-Chun.
    Basic concepts of statistical analysis applied to biological sciences. Topics include random variables, sampling distributions, parameter estimates, statistical inference.
  • Biostat 200A: Biostatistics, Professor Boscardin.
    Topics in methodology of applied statistics, such as design, analysis of variance, regression.
  • Biostat 202: Regression Analysis, Professor Boscardin.
    Additional theoretical topics in regression analysis for students concurrently enrolled in course 200A.  Topics include regression applications of matrix algebra, multivariate calculus, and statistical computing.
  • Biostat M208: Introduction to Demographic Methods (Same as Community Health Sciences M208, Economics M208, and Sociology M213A), Professor Frankenberg .
    Introduction to methods of demographic analysis. Topics include demographic rates, standardization, decomposition of differences, life tables, survival analysis, cohort analysis, birth interval analysis, models of population growth, stable populations, population projection, and demographic data sources.
  • Biostat M215: Survival Analysis (Same as Biomathematics M281), Professor Li.
    Statistical methods for analysis of survival data.
  • Biostat M234: Applied Bayesian Inference, Professor Weiss.
    Bayesian approach to statistical inference, with emphasis on biomedical applications and concepts rather than mathematical theory. Topics include large sample Bayes inference from likelihoods, noninformative and conjugate priors, empirical Bayes, Bayesian approaches to linear and nonlinear regression, model selection, Bayesian hypothesis testing and numerical methods.
  • Biostat M255: Advanced Topics and Probability in Biostatistics, Professor Dabrowska.
    Topics include conditioning, modes of convergence, basic limit results for empirical processes, von-Mises calculus, and notions of efficiency in statistics. Applications cover M-L-R estimation in two-sample and regression models, goodness of fit methods, smoothing techniques, and bootstrap.
  • Biostat M275: Advance Survival Analysis, Professor Dabrowska.
    Censoring and truncation, single sample problems, K-sample comparisons, Cox regression model, hazard rate and density estimation, estimation in Markov chains and Markov renewal processes, multivariate models, competing risks.
  • Biostat 403A: Computer Management of Health Data, Professor Sayre.
    Concepts of health data management, design and maintenance of large databases on various media as well as across networks; computer programming tools and techniques facilitating data entry, transmission, data retrieval for statistical analyses, tabulation and report generation useful to biostatisticians, health planners, and other health professionals.
  
   
• Community Health Science
  • Com Hlth M208: Introduction to Demographic Methods (Same as Biostatistics M208, Economics M208, and Sociology M213A), Professor Frankenberg.
    Introduction to methods of demographic analysis. Topics include demographic rates, standardization, decomposition of differences, life tables, survival analysis, cohort analysis, birth interval analysis, models of population growth, stable populations, population projection, and demographic data sources.
  • Com Hlth M219: Theory-Based Data Analysis, Staff.
    Translation of theory into data analytic plan, its application to real data, and interpretation of results obtained through multivariate analysis. Analysis of quantitative data using range of multivariate techniques, such as linear multiple regression and logistic regression. Analysis of theoretical problem using student quantitative data or public use data.
  
   
• Economics
  • Econ 41: Statistics for Economists, Staff.
    Introduction to probability and statistics for economists, with emphasis on rigorous arguments.
  • Econ 103: Introduction to Econometrics, Finan.
    Introduction to theory and practice of econometrics, with goal to make students effective consumers and producers of empirical research in economics. Emphasis on intuitive understanding rather than on rigorous arguments; concepts illustrated with applications in economics.
  • Econ 203A: Probability and Statistics for Econometrics, Staff.
    Provides statistical tools necessary to understand econometric techniques. Random variables, distribution and density functions, sampling, estimators, estimation techniques, hypothesis testing, and statistical inference. Use of economic problems and examples.
  • Econ M208: Introduction to Demographic Methods (Same as Biostatistics M208, Community Health Sciences M208, and Sociology M213A), Professor Frankenberg.
    Introduction to methods of demographic analysis. Topics include demographic rates, standardization, decomposition of differences, life tables, survival analysis, cohort analysis, birth interval analysis, models of population growth, stable populations, population projection, and demographic data sources.
  • Econ M231A: Econometrics: Single Equation Models, Staff.
    Linear regression model, specification error, functional form, autocorrelation, nonlinear estimation, distributed lags, nonnormality, univariate time series, qualitative dependent variables, aggregation, structural change, and errors-in-variables.
   
• Education
  • Educ 230A: Introduction to Research Design and Statistics; Professor Webb and Professor Schlackman.
    Key concepts and issues in design and conduct of social sciences research. Introduction to descriptive statistics and fundamentals of statistical inference.
  • Educ 231A: Multivariate Analysis, Professor Ender.
    Review of multiple regression analysis, analysis of covariance. Introduction to matrix algebra. Introduction to multivariate normal distribution. Multivariate analysis of variance. Linear discriminant function. Analysis of repeated measurements. Canonical correlation. Principal components.
  • Educ 231D: Advanced Quantitative Models in Nonexperimental Research: Multilevel Analysis, Staff.
    Examination of conceptual, substantive, and methodological issues in analyzing multilevel data (i.e., on individuals in organizational settings such as schools, corporations, hospitals, communities); consideration of alternative analytical models.

   

• Epidemiology
  • Epidemiology M204: Logic, Causation, and Probability (Same as Statistics M243), Professor Greenland.
    Principles of deductive logic and causal logic using counterfactuals. Principles of probability logic and probabilistic induction. Causal probability logic using directed acyclic graphs.

   

• Human Complex Systems
  • Hum CS M100: Modeling and Simulation (same as Anthropology M186 and Honors Collegium M150), Professor Nardi.
    Exploration of different approaches to modeling empirical phenomena of concern to social sciences. Topics include utility models, learning models, decision models, group competition models, and evolutionary models. Use of multiagent computer simulations and group exercises to explore emergent behaviors among individuals interacting according to models for behavior. Discussion of advantages and drawbacks of more traditional mathematical modeling. Review of alternative forms of formal representations of hypothesized processes and issues related to verification of simulations.
     
• Management
  • Mgmt 213C: Introduction to Multivariate Analysis, Professor Morrison.
    Introduction to use of multivariate models in management research to organize and represent information; interpretation of coefficients from multivariate exploratory models (e.g., principal axes and factor analysis models); survey of multivariate statistical procedures (e.g., multiple discriminate analysis, multivariate analysis of variance, canonical correlation, and confirmatory factor models).
  • Mgmt 402: Data and Decisions, Professors McCardle, Bikhchandani, Sarin, Mamer, and Stern.
    Topics include probabilities, random variables (expectation, variance, covariance, normal random variables), decision trees, estimation, hypothesis testing, and multiple regression models. Emphasis on actual business problems and data.
  
   
• Nursing
  • Nursing 207: Quantitative Research Designs of Clinical Phenomena, Professor Doering.
    Introduction to wide array of quantitative research designs for testing clinical nursing phenomena. Emphasis on dynamic interaction between research process and theory, as well as on appropriate use of experimental, quasi-experimental, and correlational designs among diverse populations.  Approaches for evaluation of validity of various research designs, with analysis of related threats to validity of each design. 
   

• Political Science
  • Pol Sci 200A: Statistical Methods I, Professor Denardo.
    Introduction to statistical analysis of political data. Methods of data analysis, estimation, and inference.
  • Pol Sci M208D: Multivariate Analysis with Latent Variables (Same as Psychology M257 and Statistics M242), Professor Bentler.
    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.
    
     
• Psychology
  • Psych 100A: Psychological Statistics, Professors McAuliffe and Marken.
    Basic statistical procedures and their application to research and practice in various areas of psychology.
  • Psych 100B: Research Methods in Psychology, Professor Firstenberg.
    Introduction to research methods and critical analysis in psychology.
  • Psych 250A: Advanced Psychological Statistics, Professor Reise.
    Basic statistical techniques as applied to design and interpretation of experimental and observational research.
  • Psych M257: Multivariate Analysis with Latent Variables (Same as Political Science M208D and Statistics M242), Professor Bentler.
    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.
  
   
• Public Policy
  • Pub Plc 203: Statistical Methods of Policy Analysis I, Professor Phillips.
    Review of statistical principles useful to policy research and analysis. Topics include descriptive statistics, expectations, univariate distribution, probability, covariance and correlations, statistical independence, random sampling, estimators, unbiasedness and efficiency, statistical inference, confidence intervals, and hypothesis testing.
  • Pub Plc M224A: Introduction to Geographic Information Systems (Same as Urban Planning M206A), Professor Estrada.
    Principles of Geographic Information Systems (GIS) and applied techniques of using spatial data for mapping and analysis. Topics include data quality, data manipulation, spatial analysis, and information systems. Use of mapping and spatial analysis to address a planning problem.
  
   
• Sociology
  • Sociol 210A: Intermediate Statistical Methods I, Professor Grannis.
    Intermediate statistical methods using computers: probability theory, sampling distributions, hypothesis testing, interval estimation, multiple regression and correlation, experimental design, analysis of variance and covariance, contingency tables, sampling theory.
  • Sociol 210C: Intermediate Statistical Methods II, Professor Sweeney.
    Survey of advanced statistical methods used in social research, with focus on problems for which classical linear regression model is inappropriate, including categorical data, structural equations, longitudinal data, incomplete and erroneous data, and complex samples.
  • Sociol 212A: Survey Data Analysis, Professor Treiman.
    Analysis and interpretation of primarily nonexperimental quantitative data, with focus on sample survey and census data. Extensive practice at utilizing statistical methods encountered in previous courses, culminating in term paper in style of "American Sociological Review" or similar journal article. Topics include simple tabular analysis, log-linear analysis, ordinary least squares regression, robust regression, binomial and multinomial logistic regression, and scale construction. Logic of analysis and problems of statistical inference, including diagnostic procedures and methods for handling complex sample survey designs.
  
   
• Statistics
  • Stats 110A: Applied Statistics, Professor Ioudina.
    Probability, distributions, expectation, estimation, central limit theorem, confidence intervals, testing.
  • Stats 112: Statistical Methods for Social Sciences, Professor Gould.
    Statistical methods in social sciences, including regression, multivariate techniques, logistic regression, and data-handling and analysis. Applications to social sciences, using profession statistical analysis software package for data analysis.
  • Stats 130A: Statistical Analysis with Stata, Professor Lew.
    How to manage and analyze quantitative data using Stata statistical software. Graphical analysis and programming and extensions to basic package.
  • Stats 154: Measurement and its Applications (Same as Psychology M144), Professor Bentler.
    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.
  • Stats 170: Introduction to Time Series Analysis, Professor Sanchez.
    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.
  • Stats CM185: Statistical Methods for Physical Sciences (Same as Atmospheric and Oceanic Sciences CM185), Staff.
    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.
  • Stats 201A: Research Design, Sampling, and Data Management, Professor Xu.
    Conditioning, Markov chains, Poisson process, Brownian motion, stationary processes, applications.
  • Stats 204: Nonparametric Function Estimation and Modeling, Professor Hansen.
    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.
  • Stats M222: Spatial Statistics, Professor Paik Schoenberg.
    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.
  • Stats 234: Statistics and Information Theory, Staff.
    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.
  • Stats 238: Vision as Bayesian Inference, Professor Yuille.
    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 as a benchmark.
  • Stats M242: Multivariate Analysis with Latent Variables (Same as Political Science M208D and Psychology M257), Professor Bentler.
    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.
  • Stats M243: Logic, Causation, and Probability (Same as Epidemiology M204), Professor Greenland.
    Principles of deductive logic and causal logic using counterfactuals. Principles of probability logic and probabilistic induction. Causal probability logic using directed acyclic graphs.
  
   
• Urban Planning
  • Urbn PL M206A: Introduction to Geographic Information Systems (Same as Public Policy M224A), Professor Estrada.
    Principles of Geographic Information Systems (GIS) and applied techniques of using spatial data for mapping and analysis. Topics include data quality, data manipulation, spatial analysis, and information systems. Use of mapping and spatial analysis to address a planning problem.
  • Urbn PL M215: Spatial Statistics (Same as Geography M272 and Statistics M222), Professor Paik Schoenberg.
    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.

Spring 2007

• Biomathemathics
  • Biomath 98T: In Silico Man: Simulation of Human Body in Biomedical Research, Professors Rovetti and Landaw.
    Use of mathematical and statistical modeling and computer simulation to analyze and understand molecular, cellular, and systems biology of human physiology. Overview of essential biological and mathematical concepts, modeling principles, and practical applications; discussion of scientific, business, regulatory, and ethical implications.
  • Biomath 110: Elements of Biomathematics, Professor Engel.
    Analysis of deterministic models. Conditions under which deterministic and probabilistic descriptions of biological phenomena are appropriate. Both approaches are applied to selected examples in physiology and biology.
  • Biomath M203: Stochastic Models in Biology, Professor Lange (Same as Human Genetics M203).
    Mathematical description of biological relationships, with particular attention to areas where conditions for deterministic models are inadequate. Examples of stochastic models from genetics, physiology, ecology, and a variety of other biological and medical disciplines.
  • Biomath 204: Biomedical Data Analysis, Professor Suchard.
    Quantity and quality of observations have been greatly affected by present-day extensive use of computers. Problem-oriented study of latest methods in statistical data analysis and use of such arising in laboratory and clinical research.
     
• Biostatistics
  • Biostat 100A: Introduction to Biostatistics, Professor Lee.
    Introduction to methods and concepts of statistical analysis. Sampling situations, with special attention to those occurring in biological sciences. Topics include distributions, tests of hypotheses, estimation, types of error, significance and confidence levels, sample size.
  • Biostat 200C: Biostatistics, Professor Wong.
    Measures of association and analysis of categorical data, theory of generalized linear models.
  • Biostat M220: Advanced Experimental Statistics, Professor Garfinkel.
    Introduction to statistics with focus on computer simulation instead of formulas. Bootstrap and Monte Carlo methods used to analyze physiological data.
  • Biostat 251: Multivariate Biostatistics, Professor Weiss.
    Multivariate analysis as used in biological and medical situations. Topics from multivariate distributions, component analysis, factor analysis, discriminant analysis, MANOVA, MANCOVA, longitudinal models with random coefficients.
  • Biostat 255: Advanced Topics and Probability in Biostatistics, Professor Dabrowska.
    Topics include conditioning, modes of convergence, basic limit results for empirical processes, von-Mises calculus, and notions of efficiency in statistics. Applications cover M-L-R estimation in two-sample and regression models, goodness of fit methods, smoothing techniques, and bootstrap.
  • Biostat 277: Robustness and Modern Nonparametrics, Professor Li.
    Topics include M-estimation, influence curves, breakdown point, bootstrap, jackknife, smoothing, nonparametric regression, generalized additive models, density estimation.
  • Biostat 406: Applied Multivariate Biostatistics, Professor Afifi.
    Use of multiple regression, principal components, factor analysis, discriminant function analysis, logistic regression, and canonical correlation in biomedical data analysis.
  • Biostat 410: Statistical Methods in Clinical Trials, Professor Lee.
    Design of studies in animals to assess antitumor response; randomization, historical controls, p-values, size of study, and stratification in human experimentation; various types of controls; prognostic factors, survivorship studies, and design of prognostic studies; organization of clinical trials administration, comparability, protocols, clinical standards, data collection and management.
   
• Economics
  • Econ 41: Statistics for Economists, Professor Guggenberger.
    Introduction to probability and statistics for economists, with emphasis on rigorous arguments.
  • Econ 103: Introduction to Econometrics, Professor Hotz.
    Introduction to theory and practice of econometrics, with goal to make students effective consumers and producers of empirical research in economics. Emphasis on intuitive understanding rather than on rigorous arguments; concepts illustrated with applications in economics.
  • Econ 232B: Topics in Econometrics: Time Series: Statistics for Economists, Professor Guggenberger.
    Stationary stochastic processes, Box/Jenkins methods, spectral analysis, forecasting, rational expectation models, analysis of macroeconomic data.
           
• Education
  • Educ 200B: Survey Research Methods in Education, Staff.
    Problems of conceptualization, organization, and gathering nonexperimental and quasi-experimental quantitative and qualitative data.
  • Educ 230A: Introduction to Research Design and Statistics, Staff.
    Key concepts and issues in design and conduct of social sciences research. Introduction to descriptive statistics and fundamentals of statistical inference.
  • Educ 230C: Linear Statistical Models in Social Science Research: Analysis of Designed Experiments, Professor Wang and Professor Choi.
    Solid and comprehensive training in experimental design and analysis methods, especially use of analysis of variance methods.
     
• Epidemiology
  • Epidem M403: Computer Management and Analysis of Health Data Using SAS (Same as Biostatistics M403B), Staff.
    Introduction to practical issues in management and analysis of health data using SAS programming language. Cross-sectional and longitudinal population-based data sets to be used throughout to illustrate principles of data management and analysis for addressing biomedical and health-related hypotheses.
     
• Health Services
  • Hlt Ser 237C: Issues in Health Services Methodologies, Professor Ettner.
    Intended to train students in statistical and economic methods used in health services research, with focus on practical application of advanced regression models.
   
• Management
  • Mgmt 465A: Quantitative Methods for Managers, Professor Mamer.
    Survey of modeling approaches to managerial planning and decisions. Emphasis on ability to recognize situations where models can be used advantageously, to work effectively with model building specialists, and to make good use of models once they have been developed.
  • Mgmt 468: Macroeconomics and Economic Forecasting, Professor Leamer.
    Macroeconomic theory and its application to business forecasting. Major economic indicators and their historical description of the U.S. economy; theoretical tools that business economists use to analyze impacts of monetary and fiscal policy; macroeconometric techniques applicable to business decisions.
   
• Political Science
  • Pol Sci 200C: Statistical Methods III, Professor Denardo.
    Statistical modeling of political processes. Topics include simultaneous equations models, discrete choice models, time-series models.
  • Pol Sci 200E: Advanced Topics in Quantitative Methods, Staff.
    Topics vary each year and have included instrumental variables principal components and scaling, models of selection, models of duration, ecological inference, and hierarchal models. Student-led presentations on relevant statistical theory and applications. Monte Carlo simulations and replications of well-known studies used to demonstrate how various models work and how they are applied in practice.
  • Pol Sci 209: Special Topics in Formal Theory and Quantitative Methods, Professor Groseclose.
    Study of nexis of formal theory and statistical methods. Students read set of papers, most of which construct theoretical model to examine political or economic phenomenon. Papers structurally estimate parameters in theoretical model.
     
• Psychology
  • Psych 100A: Psychological Statistics, Professor Nandy.
    Basic statistical procedures and their application to research and practice in various areas of psychology.
  • Psych 256: Advanced Regression Analysis, Professor Reise.
    Advanced treatment of traditional topics -- model comparison strategies, evaluation of model assumptions, outliers, mediation, moderation, categorical variable, polynomials, transformations, logistic regression.
   
• Public Policy
  • Pub Plc M224B: Advanced Geographic Information Systems, Professor Estrada (Same as Urban Planning M206B).
    Principles and skills of geographic analysis and modeling; managing, processing, and interpreting spatial data. Especially useful for students interested in environmental, demographic, suitability, and transportation-related research. Scripts (Avenue), modeling (Spatial Analyst), network analysis, and transportation modeling (TransCAD).
   
• Sociology
  • Sociol 212C: Study Design and Other Issues in Quantitative Data Analysis, Professor Mason.
    Designed for graduate and undergraduate students who have had some exposure to statistics and quantitative methods. Introduction to study design, including experimental, longitudinal, cohort, time-series designs, contextual, and other designs. Discussion of suitability of various design classes for specific analytic goals, as well as their comparative strengths and weaknesses.
   
• Statistics
  • Stats 10: Introduction to Statistical Reasoning, Professor Davis and Professor Esfandiari.
    Descriptive statistics, elementary probability, random variables, binomial and normal distributions. Large and small sample inference concerning means.
  • Stats 13: Introduction to Statistical Methods for Life and Health Sciences, Professor Schoenberg and Professor Ioudina.
    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.
  • Stats100C: Regression Analysis, Professor Christou.
    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.
  • Stats 102C: Markov Chain Monte Carlo Methods: Introduction, Staff.
    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.
  • Stats110A: Applied Statistics, Professor Ioudina.
    Probability, distributions, expectation, estimation, central limit theorem, confidence intervals, testing.
  • Stats 110B: Applied Statistics, Professor Esfandiari.
    One- and two-sample problems, goodness of fit and contingency tables, correlation and regression, analysis of variance, nonparametrics.
  • Stats 120B: Introduction to Applied Regression Analysis, Professor Xu.
    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.
  • Stats 135: Introduction to Computational Statistics with R, Professor DeLeeuw.
    Introductory examination of programming in R.
  • Stats C156: Data Management, Professor Lew.
    Proper methods by which researchers should create, document, maintain, and utilize statistical databases. Basics of raw data formats to completion of data archive.
  • Stats 170: Introduction to Time-Series Analysis, Professor Sanchez.
    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.
  • Stats C183: Statistical Models in Finance, Professor Christou.
    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).
  • Stats 238: Vision as Bayesian Inference, Professor Yuille.
    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.
  • Stats 253: Statistical Methods for Ecology and Population Biology, Professor Gould.
    Conceptual underpinnings of modern applied statistical analysis to prepare students to think critically about data and statistical models used in biological sciences.
     
• Urban Planning
  • Urbn Pl M206B: Advanced Geographic Information Systems (Same as Public Policy M224A), STAFF.
    Principles of Geographic Information Systems (GIS) and applied techniques of using spatial data for mapping and analysis. Topics include data quality, data manipulation, spatial analysis, and information systems. Use of mapping and spatial analysis to address a planning problem.
  • Urbn Pl 220B: Quantitative Analysis in Urban Planning II, STAFF.
    Introduction to concepts of statistical inference and modeling, with emphasis on urban planning applications. Topics include sampling, hypothesis testing, analysis of variance, correlation, and simple and multiple regression. Use of computer as tool in statistical analysis and modeling.

Winter 2007

• Biomathemathics
  • Biomath M211: Mathematical and Statistical Phylogenetics (Same as Human Genetics M211), Professor Suchard.
    Theoretical models in molecular evolution, with focus on phylogenetic techniques. Topics include evolutionary tree reconstruction methods, studies of viral evolution, phylogeography, and coalescent approaches. Examples from evolutionary biology and medicine. Laboratory for hands-on computer analysis of sequence data.
  • Biomath M234: Applied Bayesian Analysis (Same as Biostatistics M234), Professor Weiss.
    Bayesian approach to statistical inference, with emphasis on biomedical applications and concepts rather than mathematical theory. Topics include large sample Bayes inference from likelihoods, noninformative and conjugate priors, empirical Bayes, Bayesian approaches to linear and nonlinear regression, model selection, Bayesian hypothesis testing, and numerical methods.


• Biostatistics
  • Biostat 100B: Introduction to Biostatistics, Professor Sugar.
    Introduction to analysis of variance, linear regression, and correlation analysis.
  • Biostat 110B: Basic Biostatistics, Professor Cumberland.
    Topics include elementary analysis of variance, simple linear regression; topics related to analysis of variance and experimental designs.
  • Biostat 200B: Biostatistics, Professor Belin.
    Multiple linear regression, including model validation, influence of observations, regression diagnostics; discriminant analysis; principal components; factor analysis and clinical trials.
  • Biostat 201: Topics in Applied Regression, Professor Gornbein.
    Further studies in multiple linear regression, including applied multiple regression models, regression diagnostics and model assessment, factorial and repeated measure analysis of variance models, nonlinear regression, logistic regression, propensity scores, matching versus stratification, Poisson regression, and classification trees. Applications to biomedical and public health scientific problems.
  • Biostat M234: Applied Bayesian Analysis (Same as Biomathematics M234), Professor Weiss.
    Bayesian approach to statistical inference, with emphasis on biomedical applications and concepts rather than mathematical theory. Topics include large sample Bayes inference from likelihoods, noninformative and conjugate priors, empirical Bayes, Bayesian approaches to linear and nonlinear regression, model selection, Bayesian hypothesis testing, and numerical methods.
  • Biostat M403B: Computer Management and Analysis of Health Data Using SAS (Same as Epidemiology M403), Professor Smith.
    Introduction to practical issues in management and analysis of health data using SAS programming language. Cross-sectional and longitudinal population-based data sets to be used throughout to illustrate principles of data management and analysis for addressing biomedical and health-related hypotheses.
  • Biostat 411: Analysis of Correlated Data, Professor Afifi.
    Statistical techniques designed for analysis of correlated data, including cluster samples, multilevel models, and longitudinal studies. Computations done on SAS and Stata. Mixed models and generalized estimation equations (GEE). Emphasis on application, not theory.


• Economics
  • Econ 41: Statistics for Economists, STAFF.
    Introduction to probability and statistics for economists, with emphasis on rigorous arguments.
  • Econ 203B: Introduction to Econometrics: Single Equation Models, Professor Kyriazidou.
    Estimation of basic linear regression model, testing hypotheses, generalized least squares, serial correlation, heteroskedasticity, multicollinearity, error-in-variables, distributed lags, qualitative dependent variables, and forecasting.


• Education
  • Educ 221: Computer Analyses of Empirical Data in Education, Professor Sax.
    Designed to develop conceptual and technical skills needed for designing and executing empirical research utilizing statistical packages. Each student conducts two original studies. Equal emphasis on techniques of data analysis and interpretation of results
  • Educ 222B: Participant-observation Field Methods, The Staff.
    First of two courses on participant-observation field methods. Key skills (e.g., observation, recording, interviewing, role management, data storage) learned through classroom lectures and simulations, and by conducting actual field-based research project.
  • Educ 230B: Multiple Regression Analysis, Professor Webb.
    Solid and comprehensive training in regression-based methods for analyzing quantitative social science data.
  • Educ 231B: Factor Analysis, STAFF.
    Exploratory factor analysis, rotations, confirmatory factor analysis, multiple-group analysis.
  • Educ 231D: Advanced Quantitative Models in Nonexperimental Research:  Multilevel Analysis, Professor Seltzer.
    Examination of conceptual, substantive, and methodological issues in analyzing multilevel data (i.e., on individuals in organizational settings such as schools, corporations, hospitals, communities); consideration of alternative analytical models.


• Epidemiology
  • Epidem M403: Computer Management and Analysis of Health Data Using SAS (Same as Biostatistics M403B), Professor Smith.
    Introduction to practical issues in management and analysis of health data using SAS programming language. Cross-sectional and longitudinal population-based data sets to be used throughout to illustrate principles of data management and analysis for addressing biomedical and health-related hypotheses.


• Health Services
  • Hlt Ser 237B: Special Topics in Health Services Research Methodology, Professor Bao.
    Introduction to multivariate analysis techniques in health services research. Model specification and estimation, regression diagnostics, variable transformations, instrumental variables. Application of statistical software using large-scale national database.
  • Hlt Ser 249F:  Quality Assessment and Assurance, Professor Brook.
    Fundamental issues in quality assessment, quality assurance, and measurement of health status.


• Management
  • Mgmt 210B: Applied Stochastic Processes, Professor McCardle.
    Topics include Poisson processes, renewal theory, Markov chains, and Markov decision processes, with emphasis on problem formulation, decision making, and characterization of optimal policies. Specific applications include traditional operations research topics (inventory, queueing, maintenance, reliability), as well as several in microeconomics (search and research and development).
  • Mgmt 213B: Statistical Methods in Management, Professor Stern.
    Introduction to parameter and interval estimation, simple and multiple linear regression and correlation, fixed, random, and mixed effects analysis of variance models and nonparametric statistics, all as they apply to management studies.


• Political Science
  • Pol Sci 6: Introduction to Data Analysis, Professor Denardo.
    Introduction to collection and analysis of political data, with emphasis on application of statistical reasoning to study of relationships among political variables. Use of computer as aid in analyzing data from various fields of political science, among them comparative politics, international relations, American politics, and public administration.
  • Pol Sci 200B: Statistical Methods II, Professor Lewis.
  • Pol Sci 209: Special Topics in Formal Theory and Quantitative Methods, Professor Chwe.
    Survey of mathematical models of voting and other forms of political participation, focusing on recent approaches which try to incorporate psychological considerations.
  
  
• Psychology
  • Psych 100A: Psychological Statistics, The Staff.
    Basic statistical procedures and their application to research and practice in various areas of psychology.
  • Psych 136C:  Survey Methods in Psychology, The Staff.
    Survey research in psychology, with particular emphasis on surveys of social and political attitudes. Actual experience in systematic survey research such as that done by media polling agencies, market research companies, and academic survey research centers. Topics include survey design, sampling, interviewing techniques, response rates, questionnaire design, data coding, and analysis. Training in telephone interviewing techniques in laboratories.
  • Psych 220B:  Research Methods in Social Psychology, Professors Huo and Mann.
    Research design and methodological issues in experimental and nonexperimental social research.
  • Psych 250B: Advanced Psychological Statistics, Professor Nandy.
    Advanced experimental design and planning of investigations.
  • Pysch 255B: Item Response Theory, Professor Reise.
    Introduction to item response theory (IRT) measurement models and their application to educational and psychological data. Coverage of major IRT models, including models for dichotomous and polytomous formats.


• Public Policy
  • Pub Plc 208: Statistical Methods of Policy Analysis II, Professor Reber.
    Quantitative studies of public policy, covering regression analysis and its application to public policy questions.
     
• Social Welfare
  • Soc Wlf 285H:  Program Evaluation Research, Professor Franke.
    Discussion of differences and similarities between evaluation and other research, alternative program evaluation methods, roles and limitations of evaluation research in real world, development of proposals for feasible program evaluation research.

• Sociology
  • Sociol 113: Statistical and Computer Methods for Social Research, Professor McFarland.
    Continuation of course M18, covering more advanced statistical techniques such as multiple regression, analysis of variance, or factor analysis. Content varies. Students learn how to use computer and write papers analyzing prepared data sets.
  • Sociol 208A: Social Network Methods, Professor Bonacich
    Techniques for measuring characteristics of networks and positions in networks. Centrality of positions, centralization and density of networks, structural equivalence, cliques. Readings of exemplars of network research. Computer programs.
  • Sociol 210B: Intermediate Statistical Methods II, Professor Campbell.
    Intermediate statistical methods using computers: probability theory, sampling distributions, hypothesis testing, interval estimation, multiple regression and correlation, experimental design, analysis of variance and covariance, contingency tables, sampling theory.
  • Sociol 212B: Survey Data Analysis, Professor Treiman.
    Analysis and interpretation of primarily nonexperimental quantitative data, with focus on sample survey and census data. Extensive practice at utilizing statistical methods encountered in previous courses, culminating in term paper in style of "American Sociological Review" or similar journal article. Topics include simple tabular analysis, log-linear analysis, ordinary least squares regression, robust regression, binomial and multinomial logistic regression, and scale construction. Logic of analysis and problems of statistical inference, including diagnostic procedures and methods for handling complex sample survey designs.
  • Sociol M213B: Applied Event History Analysis (Same as Statistics M213), Professor Mare.
    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.


• Statistics
  • Stats 110A: Applied Statistics, Professor Sanchez.
    Probability, distributions, expectation, estimation, central limit theorem, confidence intervals, testing.
  • Stats 112: Statistical Reasoning, Professor Esfandiari.
    Introduction to statistical thinking and understanding, with emphasis on techniques used in social sciences. Underlying logic behind statistical procedures, role of variation in statistical thinking, strengths and limitations of statistical summaries, and fundamental inferential tools. Applications to social science investigations in laboratory, using professional statistical analysis software package.
  • Stats 120A: Introduction to Applied Regression Analysis, Professor Gould.
    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.
  • Stats 130B: Statistical Analysis with SAS, Professor Lew.
    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.
  • Stats C151: Experimental Design (Concurrently scheduled with Stat C225), Professor Xu.
    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.
  • Stats C160: Site-Specific Topics (Concurrently scheduled with Stat C260), Professor Hansen.
    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.
  • Stats C180: Introduction to Bayesian Statistics (Concurrently scheduled with Stat C236),  Professor Sabatti.
    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.
  • Stats 200B:  Applied Probability, Professor Li.
    Sufficiency, exponential families, least squares, maximum likelihood estimation, Fisher information, Cramér/Rao inequality, confidence intervals.
  • Stats 201B: Regression Analysis: Model Building, Fitting, and Criticism, Professor Paik Schoenberg.
    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.
  • Stats 232A: Statistical Modeling and Learning in Vision and Science, Professor Zhu.
    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.
  • Stats M254: Statistical Methods in Computational Biology (Same as Biomathematics M271), Professor Zhou.
    Training in probability and statistics for students interested in pursuing research in computational biology, genomics, and bioinformatics.
  • Stats 257: Design, Analysis, and Modeling for Embedded Sensing, STAFF.
    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.


• Urban Planning
  • Urbn Pl M206A: Introduction to Geographic Information Systems (Same as Public Policy M224A), STAFF.
    Principles of Geographic Information Systems (GIS) and applied techniques of using spatial data for mapping and analysis. Topics include data quality, data manipulation, spatial analysis, and information systems. Use of mapping and spatial analysis to address a planning problem.
  • Urbn Pl 220B: Quantitative Analysis in Urban Planning II, STAFF.
    Introduction to concepts of statistical inference and modeling, with emphasis on urban planning applications. Topics include sampling, hypothesis testing, analysis of variance, correlation, and simple and multiple regression. Use of computer as tool in statistical analysis and modeling.

For classes previous to Winter 2007, please see our page of Applied Statistics Courses Previously Offered at UCLA .

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