Applied Statistics Courses Offered at UCLA



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 Michael Mitchell at to let us know about additional courses (past, present or future) that should be listed here.

Spring 2006

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 233C: Statistical Methods in AIDS, Professor Cumberland.
  Coverage of methods necessary to address statistical problems in AIDS research, including projection methods for the size of AIDS epidemic and methods for estimating incubation distribution.
- Biostat M235: Causal Inference, Professor Belin (Same as Psychiatry M232).
  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 M237: Applied Genetic Modeling, Professor Sinsheimer (Biomathematics M207B and Human Genetics M207B).
  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 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 413: Introduction to Pharmaceutical Statistics, Professor Lee.
  Exploration of various types of statistical techniques used in pharmaceutical and related industries. Topics include bioassay and other assay techniques (e.g., ELISAs and FACs analysis), quality control techniques, and pharmacokinetic and pharmacodynamic modeling.

Economics
- Econ 203C: Introduction to Econometrics: Systems Models , Professor Buchinsky.
  Multivariate regression, simultaneous equation estimation, identification, and latent variables.

Education
- Educ 230A: CoIntroduction to Research Design and Statistics, The 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 Novak and Professor Ender.
  Solid and comprehensive training in experimental design and analysis methods, especially use of analysis of variance methods.
- 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.

Management
- Mgmt 212B: Decision Sciences Models II, Professor Herman.
  Broad survey of nonlinear, time-staged, and probabilistic models for managerial decision making. Application areas include finance, marketing, facilities design, production, and energy systems.
- Mgmt 269C: Quantitative Research in Marketing, Professor Bucklin.
  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.

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 Regression Analysis, Professor Honaker.
- Pol Sci 209: Applications and Tests of Formal Models of Politics, Professor Groseclose.
  Intersection of formal theory and statistical methods. By working through exemplar articles, students gain experience in drawing testable empirical implications from formal models and how to design statistical estimators that capture structural parameters of formal models.

Psychiatry and Biobehavioral Sciences
- Psyctry M232: Causal Inference, Professor Belin (Same as Biostatistics M235).
  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.

Psychology
- Psych 100A: Psychological Statistics, The Staff.
  Basic statistical procedures and their application to research and practice in various areas of psychology.
- Psych 250B: Advanced Psychological Statis, Professor Nandy.
  Advanced experimental design and planning of investigations.

Public Policy
- Pub Plc M224B: Advanced Geographic Information Systems, Professor Kwano (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 210C: Intermediate Statistical Methods III, Professor Mason.
  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 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 110A: Applied Statistics, Professor Li.
  Probability, distributions, expectation, estimation, central limit theorem, confidence intervals, testing.
- Stats 110B: Applied Statistics, Professor Yuan.
  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 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 135: Introduction to Computational Statistics with R, Professor De Leeuw.
- Stats M154: Measurement and Its Applications, Professor Bentler (Same as Psychology M144).
  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 201B: Regression Analysis: Model Building, Fitting, and Criticism, Professor Berk.
  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 C225: Experimental Design, Professor Xu (Concurrently scheduled with course C151).
  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 C236: Introduction to Bayesian Statistics, Professor Sabatti (Concurrently scheduled with course C180).
  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 M242: Multivariate Analysis with Latent Variables, Professor Bentler (Same as Political Science M208D and Psychology M257).
  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.
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 2005
- Biostatistics
  - 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, Professor Weiss (Same as Biomath M282).
    Presentation of classical and modern theories for analysis of repeated measures designs, with focus on computation and robustness.
  - Biostat M403B: Computer Management and Analysis of Health Data Using SAS, Professor Yu (Same as Epidemiology M403).
    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 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.
  - Econ 232B: System Models, Professor Kyriazidou.
    Multivariate regression, errors-in-variables, simultaneous equations, identification, proxy variables, latent variables, factor analysis of panel data, asymptotic distribution theory.
- Education
  - Educ 230B: Linear Statistical Models in Social Science Research: Multiple Regression Analysis, Professor Webb.
    Solid and comprehensive training in regression-based methods for analyzing quantitative social science data.
  - Educ 231B: Factor Analysis, Professor Thum.
    Exploratory factor analysis, rotations, confirmatory factor analysis, multiple-group analysis.
  - Educ 231D: Advanced Quantitative Models in Nonexperimental Research: Multilevel Analysis, Professor Thum.
    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.
- Political Science
  - Pol Sci 200B: Statistical Methods II, Professor Lewis.
    Applications of multiple regression in political science.
- Psychology
  - Psych M238: Survey Research Techniques in Psychocultural Studies, Professor Tucker (same as Psychiatry M238).
    Techniques for conceptualizing, conducting, and analyzing survey data; instruction in qualitative strategies for enhancing survey research on psychocultural problems.
  - Psych M257: Multivariate Analysis with Latent Variables, Professor Bentler (same as Political Science M208D and Statistics M242). 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.
- Public Policy
  - Pub Plc 208: Statistical Methods of Policy Analysis II, Professor Kane.
    Quantitative studies of public policy, covering regression analysis and its application to public policy questions.
- Sociology
  - Sociol 210B: Intermediate Statistical Methods, Professor Bonacich.
    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, focusing 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.
  - Sociology 285C, Applied Multilevel Analysis, Professor Mason
    This course will cover hierarchical stochastic parameter models, fixed effect models (in the econometric sense), marginal models, and design issues relevant to technique selection, as well as cross-classified models. The course is definitely "applied," and the goal is to get all students able to estimate models using a range of software, including as many as possible of Stata, Stata-Gllamm, HLM, MLwiN, aML, Mplus, and R/S+. Students will not be expected to use all packages; that's too much to ask. The web site will be updated soon and can be found at http://www.sscnet.ucla.edu/05W/soc285c-1/
- Statistics
  - Stats 110A: Applied Statistics, Professor Sanchez and Professor Beddo.
    Probability, distributions, expectation, estimation, central limit theorem, confidence intervals, testing.
  - Stats 110B: Applied Statistics, Professor Beddo.
    One- and two-sample problems, goodness of fit and contingency tables, correlation and regression, analysis of variance, nonparametrics.
  - Stats 120A: Introduction of 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 201B: Regression Analysis: Model Building, Fitting and Criticism, Professor Berk.
    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 C226: Resampling Methods.
    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.
Fall 2004
- Biostatistics
  - Biostat M208: Introduction to Demographic Methods, Professor Frankenberg (Same as Community Health Science M208 and Sociology M213A).
    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 M234: Applied Bayesian Inferences, Professor Weiss (Same as Biomathematics M234).
    Bayesian approach to statistical inferences, 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
- Education
  - Education 231A: Multivariate Analysis, Professor Thum
    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.
  - Education 255A: Measurement (No other information given on this course.)
- Policy Studies
  - Poly St 203: Statistical Methods of Policy Analysis I, Professor Kane
    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.
  - Poly St M218: Research Design and Methods for Social Policy, The Staff
    How to become more sophisticated consumers and producers of qualitative and quantitative policy research. In first half of course, formal principles of research design; in second half, various data collection methods, including ethnography, interviewing, and survey design.
- Political Science
  - Pol Sci 200A: Statistical Methods I, Professor Denardo
  - Pol Sci 209: Workshop in Basic Statistical Methods, Professor Zaller
    Basic statistical methods, including interaction terms in regression; missing data; variable construction and measurement error; logit, probit, ordered logit, and multinomial logit; instrumental variables; simple time series.
- Psychology
  - Psych 258: Multilevel Modeling for Daily Experience and other Nested Designs, Professor Gable
    Multilevel modeling statistical techniques, also known as mixed models and random coefficient models. These methods are useful for analyzing data with nested designs, such as students nested within classrooms, neighborhoods nested within counties, or daily experience data in which multiple measurement occasions are nested within persons.
- Sociology
  - Sociol 210A: Intermediate Statistical Methods I, Professor McFarland
    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 212A: Survey Data Analysis. Professor Treiman
    Analysis and interpretation of primarily nonexperimental quantitative data, focusing on sample survey and census data. 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
  - Stat 34: Applied Sampling, Professor Beddo
    This class gives you guidance on how to tell when a sample from a population is valid for understanding characteristics of the entire population, and how to design and analyze many different forms of sample surveys. This course will provide you with practice in solving statistical sampling problems, with the theory and practice of sampling from finite populations; simple random, stratified, and cluster sampling; basic properties of various estimators including ratio and regression estimators; error estimation for complex samples; nonresponse; reporting results, working with "clients". Exercises in this class will be solved with the statistical software packages STATA and R, and the computer program SURVEY. Various other statistical software packages designed to handle survey data will be introduced.
  - Stat 110A: Applied Statistics, Professors Dinov and Paik Schoenberg
    Probability, distributions, expectation, estimation, central limit theorem, confidence intervals, testing.
  - Stat 110B: Applied Statistics, Professor Esfandiari
    One- and two-sample problems, goodness of fit and contingency tables, correlation and regression, analysis of variance, nonparametrics.
  - Stat 170: Intro to Time Series, Professor Sanchez
    In this course we study common and specific methods for analyzing time series data that arise in atmospheric sciences, economics, finance, astronomy, oceanography, signal processing, biology, hydrology, computer science and many other areas of study. The course has an emphasis on applications and on acquiring hands on experience in analyzing time series. We will use SAS version 9. For more information see http://www.stat.ucla.edu/~jsanchez/teaching/course170.html .
Spring 2003
- Biostatistics
  - Biostat M210 Statistical Methods for Categorical Data, Professor Hirji (Same as Biomathematics M231.) Statistical techniques for analysis of categorical data; discussion and illustration of their applications and limitations.
  - Biostat 236: Modeling Continuous Longitudinal Data, Professor Weiss.
    Longitudinal data occur when the same measurement is made repeatedly on experimental units over time, inducing correlation in the measurements within an experimental unit. As compared with cross sectional data analysis, modeling of longitudinal data presents additional difficulties in that we must specify the time trend of the population mean and the correlation structure of the observations within a person, and how covariates affect both of these. See more information at http://rem.ph.ucla.edu/~rob/rm/index.html
  - Biostat 406: Applied Multivariate Biostatistics, Professor Afifi
    This course will cover the use of multiple regression, principal components, factor analysis, discriminant function analysis, logistic regression, and canonical correlation in biomedical data analysis. See http://www.ph.ucla.edu/class/biostat/406/spring04/ for more details.
  - Biostat 413 Introduction to Pharmaceutical Statistics, Professor Lee Exploration of various types of statistical techniques used in the pharmaceutical and related industries. Topics will include bioassay and other assay techniques (e.g. ELISAs and FACs analysis), quality control techniques, and pharmacokinetic and pharmacodynamic modeling.
- Education
  - Education 230C: Linear Statistical Models, Professor Ender & Professor Abedi
    This is the second course of the Ed 230B/C two course sequence. The purpose of these courses is to provide solid and comprehensive training in quantitative methods. It is designed to prepare students to carry out and interpret research using a variety of quantitative and statistical methods. It will cover key aspects of research design and statistical inference involving linear statistical models. The two quarters provide an integrated and unified approach to the application of linear statistical models in regression, analysis of variance, and experimental and quasi-experimental designs. This integrated approach will give students an understanding of how the analytic approaches are closely connected and will help them develop flexibility in applying quantitative methods correctly to a wide range of research problems. This sequence will also provide a strong foundation for further training in advanced statistical methods. For more information see http://www.gseis.ucla.edu/courses/ed230bc1/
  - Educ 231C: Applied Categorical & Nonnormal Data Analysis, Professor Ender
    This course will cover analysis with dichotomous, ordinal and multinomial (polytomous) dependent variables. Topics include contigency table analysis, logistic (logit) models, probit models, poisson models, negative binomial models, loglinear models, regression with censored data and regression with selection. See http://www.gseis.ucla.edu/courses/ed231c/231c.html for more details.
  - Educ 231E: Statistical Analysis with Latent Variables, Professor Muthen
    This course will cover similar material as last year's course, with additional material. A description of his previous course can be found at http://www.gseis.ucla.edu/faculty/muthen/ED231e/index.html . The lab session will illustrate analyses using the newly released version 3 of the Mplus program. This course will not be taught again until Spring 2006 due to Prof Muthen's upcoming sabbatical. This course will cover topics such as Growth Curve Models, Latent Class Analysis, Multilevel Modeling, and show how these analyses can be performed in an integrated framework.
- Political Science
  - Political Science 200E: Advanced Regression Analysis, Professor Honaker
    Diagnostics, robust regression, cross validation, resampling, outliers, missing data, geometry of regression, validity of assumptions, categorical dependent variables, transformation of variables. Access to Macintosh computer very helpful. For more information see http://www.sscnet.ucla.edu/04S/poliscim200e-1/ .
- Psychology
  - Psych 255A: Quantitative Aspects of Assessment, Professors Reise and Sidanius
    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.
- Sociology
  - 210C: Intermediate Statistical Methods III, 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. For more information see http://www.sscnet.ucla.edu/04S/soc210c-1/ .
- Statistics
  - Stat 34: Applied Sampling, Professor Kreuter
    This class gives you guidance on how to tell when a sample from a population is valid for understanding characteristics of the entire population, and how to design and analyze many different forms of sample surveys. This course will provide you with practice in solving statistical sampling problems, with the theory and practice of sampling from finite populations; simple random, stratified, and cluster sampling; basic properties of various estimators including ratio and regression estimators; error estimation for complex samples; nonresponse; reporting results, working with "clients". Exercises in this class will be solved with the statistical software packages STATA and R, and the computer program SURVEY. Various other statistical software packages designed to handle survey data will be introduced.
  - Stat 110A: Applied Statistics, Professor Xu
    Probability, distributions, expectation, estimation, central limit theorem, confidence intervals, testing, for more information see http://www.stat.ucla.edu/%7Ehqxu/stat110A/
  - Stat 170: Intro to Time Series, Professor Sanchez
    In this course we study common and specific methods for analyzing time series data that arise in atmospheric sciences, economics, finance, astronomy, oceanography, signal processing, biology, hydrology, computer science and many other areas of study. The course has an emphasis on applications and on acquiring hands on experience in analyzing time series. We will use a free software package called R that you can download in your computer for most of the examples. For more information see http://www.stat.ucla.edu/%7Ejsanchez/teaching/course170.html
  - Statistics 216: High Dimensional Data Analysis, Professor Li
    Dimensionality is an issue that can arise in every scientific field. Generally speaking, the difficulty lies on how to visualize a high dimensional function or data set. People often ask : "How do they look?", "What structures are there?", "What model should be used?" Aside from the differences that underlie the various scientific contexts, such kind of questions do have a common root in Statistics. This is the driving force for the study of high dimensional data analysis. This course will discuss several statistical methodologies useful for exploring voluminous data. They include Principal Component Analysis, Clustering and Classification, Tree-structured analysis, Neural Network, Hidden Markov Models, Sliced inverse regression(SIR) and principal Hessian direction (PHD). For more information see http://web.stat.ucla.edu/~kcli/stat216/index.html
  - Statistics 233: Statistical Methods in Biomedical Imaging, Professor Dinov
    See http://www.stat.ucla.edu/%7Edinov/courses_students.dir/04/Spring/Stat233.dir/STAT233.html for more details.
Winter 2003
- Community Health Sciences 219: Strategies of Multivariate Analysis, Professor Aneshensel
  See http://www.ph.ucla.edu/class/chs/chs219/ for more details
- Epidemiology 410: Management of Epidemiologic Data, Professor Cochran
  See http://web.stat.ucla.edu/~cochran/epi410/ for more details.
- Statistics 110A: Probability and Statistics for Engineers I, Professor Dinov
  See http://www.stat.ucla.edu/~dinov/courses_students.dir/04/Spring/Stat110A.dir/STAT110A.html for more information.
- Statistics 130D: Statistical Programming, Computation, and Visualization in C/C++, Professor Dinov
  See http://www.stat.ucla.edu/~dinov/courses_students.dir/04/Winter/STAT130D.dir/STAT130D_description.html for more information.
- Statistics 217B, Professor Berk. See handout for more information.
Fall 2002
- Statistics 217A, Professor Berk. See handout for more information.

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