### 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 2013

• Bioinformatics
• BIOINFO M252: ADVANCED METHODS IN COMPUTATIONAL BIOLOGY
• Biomathematics
• BIOMATH 206: INTRODUCTION TO MATHEMATICAL ONCOLOGY
• BIOMATH 208A: MODELING IN NEUROBIOLOGY FOR MATHEMATICIANS
• BIOMATH 211: MATHEMATICAL AND STATISTICAL PHYLOGENETICS
• BIOMATH 258: INTRODUCTION TO CLINICAL TRIALS
• BIOMATH M260B: METHODOLOGY IN CLINICAL RESEARCH II
• BIOMATH 265B: DATA ANALYSIS STRATEGIES II
• BIOMATH 299: SPECIAL TOPICS IN CLINICAL RESEARCH
• Biostatistics
• BIOSTAT 200A: BIOSTATISTICS
• BIOSTAT 200C: BIOSTATISTICS
• BIOSTAT M238: METHODOLOGY OF CLINICAL TRIALS
• BIOSTAT 251: MULTIVARIATE BIOSTATISTICS
• BIOSTAT 406: APPLIED MULTIVARIATE BIOSTATISTICS
• BIOSTAT 410: STATISTICAL METHODS IN CLINICAL TRIALS
• Computer Science
• COM SCI M266B: STATISTICAL COMPUTING AND INFERENCE IN VISION AND IMAGE SCIENCE
• COM SCI M296D:  INTRODUCTION TO COMPUTATIONAL CARDIOLOGY
• Economics
• ECON 203C: INTRODUCTION TO ECONOMETRICS: SYSTEMS MODELS
• ECON 211B: ECONOMICS OF UNCERTAINTY, INFORMATION, AND GAMES
• ECON 212B: TOPICS IN ADVANCE THEORY: APPLIED GAME THEORY
• ECON 213A:  GENERAL EQUILIBRIUM AND GAME THEORY
• ECON 218C: PROSEMINAR: ECONOMIC THEORY
• ECON 221C: MONETARY ECONOMICS III
• Education
• EDUC 211B: EDUCATIONAL AND PSYCHOLOGICAL MEASUREMENT: GENERALIZABILITY THEORY
• EDUC 221: COMPUTER ANALYSES OF EMPIRICAL DATA IN EDUCATION
• EDUC 230C: LINEAR STATISTICAL MODELS IN SOCIAL SCIENCE RESEARCH: ANALYSIS OF DESIGNED EXPERIMENTS
• EDUC 230CL: LINEAR STATISTICAL MODELS FOR EXPERIMENTAL RESEARCH: COMPUTER LABORATORY
• EDUC 231D: ADVANCED QUANTITATIVE MODELS IN NONEXPERIMENTAL RESEARCH: MULTILEVEL ANALYSIS
• Epidemiology
• EPIDEM 200C: METHODS III: ANALYSIS
• EPIDEM 404: ADVANCED SAS TECHNIQUES FOR MANAGEMENT AND ANALYSIS OF EPIDEMIOLOGIC DATA
• Health Policy and Management
• HLT POL 237C: ISSUES IN HEALTH SERVICES METHODOLOGIES
• Human Genetics
• HUM GEN M252: SEMINAR: ADVANCED METHODS IN COMPUTATIONAL BIOLOGY
• Management
• MGMT 264B: REGRESSION ANALYSIS WITH APPLICATIONS TO MARKETING AND FINANCE
• MGMT 463: DATA ANALYSIS AND MANAGEMENT DECISIONS UNDER UNCERTAINTY
• Mathematics
• MATH 2: FINITE MATHEMATICS
• MATH 33A: LINEAR ALGEBRA AND APPLICATIONS
• MATH 115A: LINEAR ALGEBRA
• MATH 133: INTRODUCTION TO FOURIER ANALYSIS
• MATH 142: MATHEMATICAL MODELING
• MATH 170B: PROBABILITY THEORY
• MATH 171: STOCHASTIC PROCESSES
• MATH 275C: STOCHASTIC PROCESSES
• Political Science
• POL SCI 200C: STATISTICAL METHODS III
• POL SCI 200C: STATISTICAL METHODS LABORATORY III
• POL SCI 200E: ADVANCED TOPICS IN QUANTITATIVE METHODS
• POL SCI 209: SPECIAL TOPICS IN FORMAL THEORY AND QUANTITATIVE METHODS
• Psychiatry
• PSYCTRY M232: CAUSAL INFERENCE
• Psychology
• PSYCH 250C: ADV PSYCH STATISTCS
• PSYCH 254A: COMPUTING METHODS FOR PSCHOLOGY
• Sociology
• SOCIOL 212B: QUANTITATIVE DATA ANALYSIS
• SOCIOL 239B: QUANTITATIVE RESEARCH ON SOCIAL STRATIFICATION AND SOCIAL MOBILITY
• Statistics
• STATS 100A: INTRODUCTION TO PROBABILITY
• STATS 100B: INTRODUCTION TO MATHEMATICAL STATISTICS
• STATS 100C: LINEAR MODELS
• STATS 101C: INTRODUCTION TO REGRESSION AND DATA MINING
• STATS 102C: INTRODUCTION TO MONTE CARLO METHODS
• STATS 112: STATISTICAL METHODS FOR SOCIAL SCIENCES
• STATS 170: INTRODUCTION TO TIME-SERIES ANALYSIS
• STATS 200C: LARGE SAMPLE THEORY, INCLUDING RESAMPLING
• STATS 201C: ADVANCED MODELING AND INFERENCE
• STATS 202C: MONTE CARLO METHODS FOR OPTIMIZATION
• STATS 218: STATISTICAL ANALYSIS OF NETWORKS
• STATS C283: STATISTICAL MODELS IN FINANCE

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 2013

• Bioinformatics
• BIOINFO M224- Computational Genetics
• BIOINFO M252 - Advanced Methods in Computational Biology.
• Biomathematics
• BIOMATH M203 - Stochastic Models in Biology
• BIOMATH 206 - Introduction to Mathematical Oncology
• BIOMATH 207B - Applied Genetic Modeling
• BIOMATH M230 - Computed Tomography: Theory and Applications
• BIOMATH 265A - Data Analysis Strategies I
• BIOMATH 265A - Applied Regression Analysis in Medical Sciences
• BIOMATH M270 - Optimal Parameter Estimation and Experiment Design for Biomedical Systems
• Biostatistics
• BIOSTAT 200B - Biostatistics
• BIOSTAT 201B - Topics in Applied Regression
• BIOSTAT 202B - Topics in Estimation
• BIOSTAT M237 - Applied Genetic Modeling
• BIOSTAT 245 - Advanced Seminar: Biostatistics
• BIOSTAT 250B - Linear Statistical Models
• BIOSTAT 256 -Advanced Methods of Mathematical Statistics
• BIOSTAT 288 - Seminar: Statistics in AIDS
• BIOSTAT M403B - Computer Management and Analysis of Health Data Using SAS
• BIOSTAT 411 - Analysis of Correlated Data
• BIOSTAT 414 - Principles of Sampling
• Computer Science
• COM SCI M266A - Statistical Modeling and Learning in Vision and Science
• Economics
• ECON 203B - Introduction to Econometrics: Single Equation Models
• ECON 231A - Econometrics: Single Equation Models
• ECON 231B - System Models
• Education
• EDUC 230B - Linear Statistical Models in Social Science Research: Multiple Regression Analysis
• EDUC 230BL - Linear Statistical Models: Computer Laboratory
• Epidemiology
• EPIDEM M216 - Applied Sampling
• Health Policy and Management
• HLT POL 239B - Advanced Topics in Decision Analysis and Cost-Effectiveness
• Human Genetics
• HUM GEN M203 - Stochastic Models in Biology
• HUM GEN M207B - Applied Genetic Modeling
• HUM GEN 210 - Topics in Genomics
• HUM GEN CM224 - Computation Genetics
• HUM GEN 236B - Advanced Human Genetics B: Statistical Aspects
• HUM GEN M252 - Seminar: Advanced Methods in Computational Biology
• Management
• MGMT 269C - Quantitative Research in Marketing
• Mathematics
• MATH 33A - Linear Algebra and Applications
• MATH 115A - Linear Algebra
• MATH 115B - Linear Algebra
• MATH 170A - Probability Theory
• MATH 170B - Probability Theory
• MATH 180 - Combinatorics
• MATH 270B - Mathematical Aspects of Scientific Computing: Computational Linear Algebra
• MATH 275B - Probability Theory
• MATH 285N - Seminar: Combinatorics
• Nursing
• NURSING 203A - Basic Statistics and Fundamentals for Analysis
• NURSING 205B - Advanced Qualitative Research Methodology I
• Political Science
• POL SCI 200B - Statistical Methods II
• POL SCI 200D - Quantitative Methods in Politics
• POL SCI M208D - Multivariate Analysis with Latent Variables
• POL SCI 209 - Special Topics in Formal Theory and Quantitative Methods
• Psychology
• PSYCH 250B - Advanced Psychological Statistics
• PSYCH M257 - Multivariate Analysis with Latent Variables
• PSYCH 258 - Special Problems in Psychological Statistics
• Public Policy
• PUB PLC 208 - Statistical Methods of Policy Analysis II
• Sociology
• SOCIOL 210B - Intermediate Statistical Methods II
• SOCIOL 212A - Quantitative Data Analysis (Covers methods for handling complex survey design)
• SOCIOL 213B - Applied Event History Analysis
• SOCIOL 289A - Practicum in Conversation Analysis: Data Analysis
• Statistics
• STATS 100A - Introduction to Probability
• STATS 100B - Introduction to Mathematical Statistics
• STATS 101B - Introduction to Data Analysis and Regression
• STATS 102B - Introduction to Computation and Optimization for Statistics
• STATS M154 - Measurement and Its Applications
• STATS C155 - Applied Sampling
• STATS C173 - Applied Geostatistics
• STATS 200B - Theoretical Statistics
• STATS 201B - Regression Analysis: Model Building, Fitting, and Criticism
• STATS 202B - Matrix Algebra and Optimization
• STATS C216 - Social Statistics
• STATS M232A - Statistical Modeling and Learning in Vision and Science
• STATS 238 - Vision as Bayesian Inference
• STATS M242 - Multivariate Analysis with Latent Variables
• STATS CM248 - Applied Sampling
• STATS C273 - Applied Geostatistics
• STATS 285 - Seminar: Computing for Statistics
• STATS 292 - Graduate Student Statistical Packages Seminar

### Fall 2012

• Bioinformatics
• BIOINFO M252: Seminar: Advanced Methods in Computational Biology
• BIOINFO M260A: Introduction to Bioinformatics
• BIOINFO M271: Statistical Methods in Computational Biology
• Biomathematics
• BIOMATH 106: Introduction to Cellular Modeling
• BIOMATH 170A: Introductory Biomathematics for Medical Investigators
• BIOMATH 200: Research Frontiers in Biomathematics
• BIOMATH 201: Deterministic Models in Biology
• BIOMATH M207A: Theoretical Genetic Modeling
• BIOMATH 210: Optimization Methods in Biology
• BIOMATH M234: Applied Bayesian Inference
• BIOMATH M260A: Methodology in Clinical Research I
• BIOMATH M260C: Methodology in Clinical Research III
• BIOMATH M261: Responsible Conduct of Research Involving Humans
• BIOMATH M271: Statistical Methods in Computational Biology
• BIOMATH M281: Survival Analysis
• BIOMATH 299: Special Topics in Clinical Research
• Biostatistics
• BIOSTAT 100A: Introduction to Biostatistics
• BIOSTAT 110A: Basic Biostatistics
• BIOSTAT 201A: Topics in Applied Regression
• BIOSTAT 202A: Theoretical Principles of Biostatistics
• BIOSTAT M208: Introduction to Demographic Methods
• BIOSTAT M215: Survival Analysis
• BIOSTAT M234: Applied Bayesian Inference
• BIOSTAT 245: Advanced Seminar: Biostatistics
• BIOSTAT 250A: Linear Statistical Models
• BIOSTAT 255: Advanced Probability in Biostatistics
• BIOSTAT M272: Theoretical Genetic Modeling
• BIOSTAT 273: Classification and Regression Trees (CART) and Other Algorithms
• BIOSTAT 400: Field Studies in Biostatistics
• BIOSTAT 402B: Biostatistical Consulting
• BIOSTAT 403A: Computer Management of Health Data
• BIOSTAT 409: Doctoral Statistical Consulting Seminar
• Computational and Systems Biology
• C&S BIO M184: Introduction to Computational and Systems Biology
• C&S BIO M186: Computational Systems Biology: Modeling and Simulation of Biological Systems
• Economics
• ECON 200: Mathematical Methods in Economics
• ECON 203A: Probability and Statistics for Econometrics
• ECON 231A: Econometrics: Single Equation Models
• ECON 231B: System Models
• Education
• EDUC 211C: Advanced Item Response Theory
• EDUC 230A: Introduction to Research Design and Statistics
• EDUC 230AL: Introduction to Research Design and Statistics: Computer Laboratory
• EDUC 231A: Toolkit for Quantitative Methods Research
• Epidemiology
• EPIDEM M204: Logic, Causation, and Probability
• EPIDEM M403: Computer Management and Analysis of Health Data Using SAS
• Human Genetics
• HUM GEN M207A: Theoretical Genetic Modeling
• HUM GEN M252: Seminar: Advanced Methods in Computational Biology
• HUM GEN M260A: Introduction to Bioinformatics
• Linguistics
• LING 208: Mathematical Structures in Language I
• Mathematics
• MATH 3C: Probability for Life Sciences Students
• MATH 33A: Linear Algebra and Applications
• MATH 115A: Linear Algebra
• MATH 142: Mathematical Modeling
• MATH 164: Optimization
• MATH 170A: Probability Theory
• MATH 275A: Probability Theory
• Mechanical and Aerospace Engineering
• MECH&AE C175A: Probability and Stochastic Processes in Dynamical Systems
• MECH&AE 182A: Mathematics of Engineering
• MECH&AE C271A: Probability and Stochastic Processes in Dynamical Systems
• Nursing
• NURSING 207: Quantitative Research Designs of Clinical Phenomena
• Political Science
• POL SCI 200A: Statistical Methods I
• POL SCI 209: Special Topics in Formal Theory and Quantitative Methods
• Psychology
• PSYCH 250A: Advanced Psychological Statistics
• PSYCH 255A: Quantitative Aspects of Assessment
• PSYCH 265: Computational Methods for Neuroimaging
• Sociology
• SOCIOL 210A: Intermediate Statistical Methods I
• Statistics
• STATS 10: Introduction to Statistical Reasoning
• STATS 13: Introduction to Statistical Methods for Life and Health Sciences
• STATS 100A: Introduction to Probability
• STATS 100B: Introduction to Mathematical Statistics
• STATS 101A: Introduction to Design and Analysis of Experiment
• STATS 102A: Introduction to Computational Statistics with R
• STATS 112: Statistical Methods for Social Sciences
• STATS 130: Getting Up to Speed with SPSS, Stata, SAS, and R
• STATS 200A: Applied Probability
• STATS 201A: Research Design, Sampling, and Analysis
• STATS 202A: Statistics Programming
• STATS M231: Pattern Recognition and Machine Learning
• STATS M243: Logic, Causation, and Probability
• STATS M254: Statistical Methods in Computational Biology

### Summer 2012

• Statistics: Session A
• STATS 10: Introduction to Statistical Reasoning
• STATS 12: Introduction to Statistical Methods for Geography and Environmental Studies
• STATS 100A: Introduction to Probability
• STATS 100C: Linear Models
• STATS 101A: Introduction to Design and Analysis of Experiment
• Statistics: Session B
• STATS 10: Introduction to Statistical Reasoning
• STATS 13: Introduction to Statistical Methods for Life and Health Sciences
• STATS 100B: Introduction to Mathematical Statistics

### Spring 2012

• Bioinformatics
• BIOINFO M252: Advanced Methods in Computational Biology
• Biomathematics
• BIOMATH 202: BIOLOGICAL SYSTEMS: STRUCTURE, FUNCTION, EVOLUTION
• BIOMATH 204: BIOMEDICAL DATA ANALYSIS
• BIOMATH 208A: MODELING IN NEUROBIOLOGY FOR MATHEMATICIANS
• BIOMATH 258: INTRODUCTION TO CLINICAL TRIALS
• BIOMATH M260B: METHODOLOGY IN CLINICAL RESEARCH II
• BIOMATH M262: COMMUNICATION OF SCIENCE
• BIOMATH 265B: DATA ANALYSIS STRATEGIES II
• BIOMATH 299: SPECIAL TOPICS IN CLINICAL RESEARCH
• Biostatistics
• BIOSTAT 200A: BIOSTATISTICS
• BIOSTAT 200C: BIOSTATISTICS
• BIOSTAT M238: METHODOLOGY OF CLINICAL TRIALS
• BIOSTAT 251: MULTIVARIATE BIOSTATISTICS
• BIOSTAT 406: APPLIED MULTIVARIATE BIOSTATISTICS
• BIOSTAT 245: ADVANCED SEMINAR: BIOSTATISTICS
• BIOSTAT 413: INTRODUCTION TO PHARMACEUTICAL STATISTICS
• Economics
• ECON 203C: INTRODUCTION TO ECONOMETRICS: SYSTEMS MODELS
• ECON 211B: ECONOMICS OF UNCERTAINTY, INFORMATION, AND GAMES
• ECON 218C: PROSEMINAR: ECONOMIC THEORY
• Education
• EDUC 211A: EDUCATIONAL AND PSYCHOLOGICAL MEASUREMENT: UNDERLYING THEORY AND PRACTICE
• EDUC 221: COMPUTER ANALYSES OF EMPIRICAL DATA IN EDUCATION
• EDUC 230C: LINEAR STATISTICAL MODELS IN SOCIAL SCIENCE RESEARCH: ANALYSIS OF DESIGNED EXPERIMENTS
• Epidemiology
• EPIDEM 200C: METHODS III: ANALYSIS
• Human Genetics
• HUM GEN CM124: COMPUTATIONAL GENETICS
• HUM GEN CM224: COMPUTATIONAL GENETICS
• HUM GEN M252: SEMINAR: ADVANCED METHODS IN COMPUTATIONAL BIOLOGY
• Management
• MGMT 269C: QUANTITATIVE RESEARCH IN MARKETING
• Mathematics
• MATH 2: FINITE MATHEMATICS
• MATH 33A: LINEAR ALGEBRA AND APPLICATIONS
• MATH 115A: LINEAR ALGEBRA
• Political Science
• POL SCI 200C: STATISTICAL METHODS III
• POL SCI 200E: ADVANCED TOPICS IN QUANTITATIVE METHODS
• POL SCI 209: SPECIAL TOPICS IN FORMAL THEORY AND QUANTITATIVE METHODS
• Psychiatry
• PSYCTRY M232: CAUSAL INFERENCE
• Psychology
• PSYCH 250C: ADV PSYCH STATISTCS
• Sociology
• SOCIOL 239B: QUANTITATIVE RESEARCH ON SOCIAL STRATIFICATION AND SOCIAL MOBILITY
• Statistics
• STATS 100A: INTRODUCTION TO PROBABILITY
• STATS 100B: INTRODUCTION TO MATHEMATICAL STATISTICS
• STATS 100C: LINEAR MODELS
• STATS 112: STATISTICAL METHODS FOR SOCIAL SCIENCES
• STATS 200C: LARGE SAMPLE THEORY, INCLUDING RESAMPLING
• STATS 201C: ADVANCED MODELING AND INFERENCE
• STATS 202C: MONTE CARLO METHODS FOR OPTIMIZATION
• STATS 218: STATISTICAL ANALYSIS OF NETWORKS
• STATS C283: STATISTICAL MODELS IN FINANCE

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 2012

• Bioinformatics
• BIOINFO M252 - Advanced Methods in Computational Biology.
• Biomathematics
• BIOMATH M203 - Stochastic Models in Biology
• BIOMATH 206 - Introduction to Mathematical Oncology
• BIOMATH 213 - Modeling Vascular Networks
• BIOMATH 220 - Kinetic and Steady State Models in Pharmacology and Physiology
• BIOMATH M230 - Computed Tomography: Theory and Applications
• BIOMATH 265A - Data Analysis Strategies I
• BIOMATH M270 - Optimal Parameter Estimation and Experiment Design for Biomedical Systems
• Biostatistics
• BIOSTAT 200B - Biostatistics
• BIOSTAT 201B - Topics in Applied Regression
• BIOSTAT M208 - Introduction to Demographic Methods
• BIOSTAT 212 - Distribution Free Methods
• BIOSTAT 233 - Statistical Methods in AIDS
• BIOSTAT M236 - Longitudinal Data
• BIOSTAT M237 - Applied Genetic Modeling
• BIOSTAT 245 - Advanced Seminar: Biostatistics
• BIOSTAT 250B - Linear Statistical Models
• BIOSTAT 276 - Inferential Techniques that Use Simulation
• BIOSTAT M403B - Computer Management and Analysis of Health Data Using SAS
• Ecology
• EE BIOL C219 - Mathematical and Computational Modeling in Ecology.
• Economics
• ECON 203B - SINGLE EQUATION MODELS\
•  ECON 231B - System Models
• Education
• EDUC 230B - Linear Statistical Models in Social Science Research: Multiple Regression Analysis
• EDUC 230BL - Linear Statistical Models: Computer Laboratory
• EDUC M231B - Factor Analysis
• EDUC 231D - Advanced Quantitative Models in Nonexperimental Research: Multilevel Analysis
• Epidemiology
• EPIDEM M216 - Applied Sampling
• Human Genetics
• HUM GEN M203 - Stochastic Models in Biology
• HUM GEN M207B - Applied Genetic Modeling
• HUM GEN 210 - Topics in Genomics
• HUM GEN 236B - Advanced Human Genetics B: Statistical Aspects
• HUM GEN M252 - Seminar: Advanced Methods in Computational Biology
• Mathematics
• MATH 33A - Linear Algebra and Applications
• MATH 115A - Linear Algebra
• MATH 115B - Linear Algebra
• MATH 170A - Probability Theory
• MATH 170B - Probability Theory
• MATH 180 - Combinatorics
• MATH 270B - Mathematical Aspects of Scientific Computing: Computational Linear Algebra
• MATH 275B - Probability Theory
• Political Science
• POL SCI 200B - Statistical Methods II
• POL SCI 200D - Quantitative Methods in Politics
• POL SCI M208D - Multivariate Analysis with Latent Variables
• Psychology
• PSYCH 250B - Advanced Psychological Statistics
• PSYCH M253 - Factor Analysis
• PSYCH M257 - Multivariate Analysis with Latent Variables
• PSYCH 258 - Special Problems in Psychological Statistics
• PSYCH 259 - Quantitative Methods in Cognitive Psychology
• Sociology
• SOCIOL 210B - Intermediate Statistical Methods II
• Statistics
• STATS 100A - Introduction to Probability
• STATS 100B - Introduction to Mathematical Statistics
• STATS 100C - Linear Models
• STATS 101B - Introduction to Data Analysis and Regression
• STATS 102B - Introduction to Computation and Optimization for Statistics
• STATS 130 - Getting Up to Speed with SPSS, Stata, SAS, and R
• STATS C151 - Experimental Design
• STATS M154 - Measurement and Its Applications
• STATS C155 - Applied Sampling
• STATS C173 - Applied Geostatistics
• STATS 200B - Theoretical Statistics
• STATS 201B - Regression Analysis: Model Building, Fitting, and Criticism
• STATS 202B - Matrix Algebra and Optimization
• STATS C216 - Social Statistics
• STATS C225 - Experimental Design
• STATS M232A - Statistical Modeling and Learning in Vision and Science
• STATS 238 - Vision as Bayesian Inference
• STATS M242 - Multivariate Analysis with Latent Variables
• STATS CM248 - Applied Sampling
• STATS C273 - Applied Geostatistics
• STATS 285 - Seminar: Computing for Statistics
• STATS 292 - Graduate Student Statistical Packages Seminar

### Fall 2011

• Biomathematics
• Biomath 210: Optimization Methods in Biology, Professor Lange.
Modern computational biology relies heavily on finite-dimensional optimization. Survey of theory and numerical methods for discrete and continuous optimization, with applications from genetics, medical imaging, pharmacokinetics, and statistics.
• Biomath M260A: Methodology in Clinical Research I, Professors Elashoff and Piantadosi.
Presentation of principles and practices of major disciplines underlying clinical research methodology, such as biostatistics, epidemiology, pharmacokinetics.
• Biomath M260C: Methodology in Clinical Research III, Professor Seeman.
Presentation of principles and practices of major disciplines underlying clinical research methodology, such as biostatistics, epidemiology, pharmacokinetics.
• 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 Brookmeyer.
Basic concepts of statistical analysis applied to biological sciences. Topics include random variables, sampling distributions, parameter estimates, statistical inference.
• Biostat 200A: Biostatistics, Professor Crespi-Chun.
Topics in methodology of applied statistics, such as design, analysis of variance, regression.
• Biostat 201A: Applied Regression, Professor Sugar.
Topics in linear regression and other related methods. When and how to use linear regression and related methods and how to properly interpret results. Heavy emphasis on practical application as opposed to theoretical development.
• Biostat 212: Distribution-free Methods, Staff.
Theory and application of distribution free methods in biostatistics.
• Biostat 213: Introduction to Computational Methods in Biostatistics, Professor Telesca
Introduction to computational methods for biostatistical inference: simulation techniques, numerical integration, numerical optimization.
• Biostat M215: Survival Analysis, Professor Li
Statistical methods for analysis of survival data.
• Biostat 238: Methodology of Clinical Trials, Staff
Methodological principles of clinical trials, actual practice and principles of trials. Considerable focus on phase two trials and multiclinical phase three trials. Emphasis on major inferential issues.
• Biostat 250A: 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 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 M273: Classification and Regression Trees (CART) and other algorithms, Staff.
Instruction in use of statistical tools in analysis of large datasets. Classification and regression trees as well as other adaptive algorithms. Implementation of CART software and other programs to real datasets.
• Biostat 295: Application of Statistical Theories in Biomedical Research, Staff.
Review of statistical theories essential to biostatistics. Illustration of applications by examples. Topics include delta method, order statistics, asymptotic properties of MLEs, iterative algorithms for MLEs, generalized likelihood ratio tests for categorical data, and transformations.
• 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 M213: Research in Community and Patient Health Education, Professor Morisky.
Application of conceptual, theoretical, and evaluation skills to community-based health education risk-reduction programs. Computer applications, data management, and research methodologies taught through microcomputer and mainframe computer management and analysis of program databases.
• Com Hlth M218: Questionnaire Design and Administration, Professor Bourque.
Designing, testing, field use, and administration of data collection instruments, with particular emphasis on questionnaires.

• Economics
• Econ 41: Statistics for Economists, Professors Hahn and Rojas.
Introduction to probability and statistics for economists, with emphasis on rigorous arguments.
• Econ 103: Introduction to Econometrics, Professor Buchinsky.
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, Professor Matzkin.
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 M231A: Econometrics: Single Equation Models, Professor Matzkin.
Linear regression model, specification error, functional form, autocorrelation, nonlinear estimation, distributed lags, nonnormality, univariate time series, qualitative dependent variables, aggregation, structural change.

• Education
• Educ 230A: Introduction to Research Design and Statistics, Professor Webb.
Key concepts and issues in design and conduct of social sciences research. Introduction to descriptive statistics and fundamentals of statistical inference.

• 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.
• Epidemiology M218: Questionnaire Design and Administration (Same as Com Hlth M218), Professor Bourque.
Designing, testing, field use, and administration of data collection instruments, with particular emphasis on questionnaires.
• Epidemiology 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.
• 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 Sarin, Barz, Bikhchandani, and Mamer.
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 Williams.
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 6: Introduction to Data Analysis, Professor Zaller.
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 200A: Statistical Methods I, Professor Denardo.
Introduction to statistical analysis of political data.

• 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 250A: Advanced Psychological Statistics, Professor Reise.
Basic statistical techniques as applied to design and interpretation of experimental and observational research.
• Psych 258: Special Problems in Psychological Statistics, Professor Krull.
Special problems in psychological statistics and data analysis.
• Psych 265: Computational Methods for Neuroimaging, Professor Monti.
Theory and practice of processing and analysis of functional MRI data. Topics include image registration, preprocessing and quality control, statistical modeling and inference, multivariate analysis, and machine learning methods.

• 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 20: Introduction to Sociological Research Methods, Professor Stivers.
Introduction to methods used in contemporary sociological research, with focus on issues of research design, data collection, and analysis of data.
• Sociol 113: Statistical and Computer Methods for Social Research, Professor Joshi.
Continuation of Statistics 10, covering more advanced statistical techniques such as multiple regression, analysis of variance, or factor analysis.
• Sociol 210A: Intermediate Statistical Methods I, Professor Rossman.
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.

• Statistics
• Stats 10: Introduction to Statistical Reasoning, Professors Almohalwas and Esfandiari.
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 Christou.
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 100A: Introduction to Probability, Professors Sanchez, Wu, and Christou.
Probability distributions, random variables, vectors, and expectation.
• Stats 100B: Introduction to Mathematical Statistics, Professor Sanchez.
Probability distributions, random variables, vectors, and expectation.
• Stats 101A: Introduction to Design and Analysis of Experiment, Professor Gould.
Fundamentals of collecting data, including components of experiments, randomization and blocking, completely randomized design and ANOVA, multiple comparisons, power and sample size, and block designs.
• Stats 102A: Introduction to Computational Statistics with R, Professor Lew.
Introduction to programming and data analysis in R.
• Stats 112: Statistical Methods for Social Sciences, Professor Almohalwas.
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 200A: Applied Probability, Professor Wu.
Simulation, renewal theory, martingale, and selected topics from queuing, reliability, speech recognition, computational biology, mathematical finance, epidemiology.
• Stats 201A: Research Design, Sampling, and Data Management, Professor Xu.
Conditioning, Markov chains, Poisson process, Brownian motion, stationary processes, applications.
• Stats 202A: Statistics Programming, Professor Paik Schoenberg.
Topics include programming environments/languages such as UNIX, UNIX shell, Python, R, and Processing and data technologies/formats such as relational databases/SQL and XML, with emphasis on complex data types, including large collections of textual data, GPS traces, network logs, and various online sources.
• 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 M231: Pattern Recognition and Machine Learning, Professor Zhu.
Fundamental concepts, theories, and algorithms for pattern recognition and machine learning that are used in computer vision, image processing, speech recognition, data mining, statistics, and computational biology. Topics include Bayesian decision theory, parametric and nonparametric learning, clustering, complexity (VC-dimension, MDL, AIC), PCA/ICA/TCA, MDS, SVM, boosting.
• 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.
• Stats M254: Statistical Methods in Computational Biology, Professor Zhou.
Introduction to statistical methods developed and widely applied in several branches of computational biology, such as gene expression, sequence alignment, motif discovery, comparative genomics, and biological networks, with emphasis on understanding of basic statistical concepts and use of statistical inference to solve biological problems.

• 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.

Summer 2011

• Biostatistics
• Biostat 100A: Introduction to Biostatistics, Professor Sayre.
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.

• Economics
• Econ 41: Statistics for Economists, Professors Rojas and Huang.
Introduction to probability and statistics for economists, with emphasis on rigorous arguments.

• Health Services
• Health Services 237A: Special Topics in Health Services Research Methodology, Professors Mangione and Ong.
Approaches to conceptualization, modeling, design, literature reviews, sampling, data collection, and research.

• Psychology
• Psych 100A: Psychological Statistics, Professors Ainsworth and Dehardt.
Basic statistical procedures and their application to research and practice in various areas of psychology.
• Psych 100B: Research Methods in Psychology, Professors Geiselman and 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.

• Sociology
• Sociol 20: Introduction to Sociological Research Methods, Professors McKay and Saguy.
Introduction to methods used in contemporary sociological research, with focus on issues of research design, data collection, and analysis of data.
• Sociol 113: Statistical and Computer Methods for Social Research, Professors Feinstein, Yuan, and Saguy.
Continuation of Statistics 10, 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.

• Statistics
• Stat 10: Introduction to Statistical Reasoning, Professors Lew and Almohalwas.
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.
• Stat 12: Introduction to Statistical Methods for Geography and Environmental Studies, Professor Almohalwas.
Introduction to statistical thinking and understanding, with emphasis on techniques used in geography and environmental science. Underlying logic behind statistical procedures, role of variation in statistical thinking, strengths and limitations of statistical summaries, and fundamental inferential tools. Emphasis on applications in geography and environmental science in laboratory work using professional statistical analysis package, including spatial statistics.
• Stat 13: Introduction to Statistical Methods for Life and Health Sciences, Professor Christou.
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.
• Stat 100A: Introduction to Probability, Professor Sanchez.
Probability distributions, random variables, vectors, and expectation.
• Stat 100B: Introduction to Mathematical Statistics, Professor You.
Survey sampling, estimation, testing, data summary, one- and two-sample problems.
• Stat 100C: Linear Models, Professor Sanchez.
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.
• Stat 101A: Introduction to Design and Analysis of Experiments, Professor Esfandiari.
Fundamentals of collecting data, including components of experiments, randomization and blocking, completely randomized design and ANOVA, multiple comparisons, power and sample size, and block designs.
• Stat 112: Statistical Methods for 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.

### Spring 2011

• Biomathemathics
• Biomath 258: Introduction to Clinical Trials, Staff.
Introduction to basic principles of good clinical trial design, trial implementation, and analysis.
• Biomath M260B: Methodology in Clinical Research II, Professor Elashoff.
Continuation of course 265A; use of SAS computer language.
• Biomath 265B: Data Analysis Strategies II, Staff.
Continuation of course 265A; use of SAS computer language.
• Biomath 266: Advanced Biostatistics, Professors Tseng and Liang.
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 Dabrowska.
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 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 230: Statistical Graphics, Professor Weiss.
Graphical data analysis emphasizes use of visual displays of quantitative data to gain insight into data structure by exploring patterns and relationships, and to enhance classical numerical analyses, especially assumption validity checking. Principles of graph construction, graphical methods, and perception issues.
• Biostat M232: Analysis of Incomplete Data, Professor Belin.
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.
• Biostat 251: Multivariate Biostatistics, Professor Telesca.
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 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 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 M403B: 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.
• 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 Rojas.
Introduction to probability and statistics for economists, with emphasis on rigorous arguments.
• Econ 103: Introduction to Econometrics, Professor Casanova Rivas.
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 Hahn and Professor Dubin.
Multivariate regression, simultaneous equation estimation, identification, and latent variables.

• Education
• Educ 211B: Educational and Psychological Measurement: Generalizability Theory, Professor Webb.
Basic and advanced topics in use of generalizability theory to address problems in measurement.
• 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 230C: Linear Statistical Models in Social Science Research: Analysis of Designed Experiments, Professor Martinez-Fernandez.
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.
Solid and comprehensive training in experimental design and analysis methods, especially use of analysis of variance methods.

• Epidemiology
• Epidem 200C: Methods III: Analysis, Professor Arah.
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 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.

• Health Services
• Hlt Ser 237C: Issues in Health Services Methodologies, Professor Ponce.
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 201B: Econometrics and Business Forecasting, Professor Nickelsburg.
Development of standard topics in applied econometric modeling. Emphasis on assumptions underlying classical normal linear regression model, special problems in application, and interpretation of results. Practical applications extensively developed in student projects.
• Mgmt 407: Spreadsheet Modeling, Professor Herman.
Introduction to uses of analytical methods for making strategic, tactical, and operational decisions arising from accounting, finance, marketing, and production, with focus on three key areas in problem solving: formal problem definition, computer model formulation, alternatives evaluation.

• 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, Professor 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, Professors Groseclose and O'Neill.
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 Reise.
Basic statistical procedures and their application to research and practice in various areas of psychology.
• Psych 100B: Research Methods in Psychology, Professor Bjork.
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 250C: Advanced Psychological Statistics, Professor Krull.
Review of traditional topics in correlation and regression analyses, including model comparison strategies, evaluation of model assumptions, testing mediation and moderation hypotheses, working with categorical variables, general linear model, and logistic regression.

• Public Policy
• Pub Plc M224B: Advanced Geographic Information Systems, Professors Kawano and Brozen.
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).

• Statistics
• Stats 10: Introduction to Statistical Reasoning, Professors Almohalwas, Cetinkaya, and Rojas.
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 12: Introduction to Statistical Methods for Geography and Environmental Studies, Professor Almohalwas.
Introduction to statistical thinking and understanding, with emphasis on techniques used in geography and environmental science. Underlying logic behind statistical procedures, role of variation in statistical thinking, strengths and limitations of statistical summaries, and fundamental inferential tools. Emphasis on applications in geography and environmental science in laboratory work using professional statistical analysis package, including spatial statistics.
• Stats 13: Introduction to Statistical Methods for Life and Health Sciences, Professor Hansen.
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 100A: Introduction to Probability, Professor Christou.
Probability distributions, random variables, vectors, and expectation.
• Stats100C: Linear Models, Professor Sanchez.
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 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 102C: Introduction to Monte Carlo Methods, 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. Metropolis and Gibbs sampling algorithms. Extensions as simulated tempering. Theoretical understanding of methods and their implementation in concrete computational problems.
• Stats 105: Statistics for Engineers, Professor Xu.
Foundation of basic concepts and techniques of statistics. Topics include sampling distributions, statistical estimation (including maximum likelihood estimation), statistical intervals, and hypothesis testing, with emphasis on application of these concepts. Discussion of methods for checking whether assumptions required for mathematical foundations are appropriate for given set of data.
• Stats 116/C216: Social Statistics, Professor Lew.
Designed for social sciences graduate students and advanced undergraduate students seeking training in data issues and methods employed in social sciences.
• Stats 157: Probability and Statistics Data Modeling and Analysis Using SOCR, Professor Dinov.
Probability and statistics topics in data-driven and interactive manner using open Internet resources. Varieties of data, study-designs, and applications arising from biomedical, research, and simulated data to prepare students for innovative multidisciplinary research. Use of Statistics Online Computational Resource (SOCR).
• 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 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 Yuille.
Description of Markov chain Monte Carlo (MCMC) sampling techniques, with emphasis on optimization and statistical estimation. Topics include Gibbs samplers, Metropolis/Hastings importance sampling, and simulated annealing. Alternative optimization techniques, including Newton/Raphson, dynamic programming, belief propagation, and variational methods.
• Stats M213: Event History Analysis, 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.

• Urban Planning
• Urbn Pl M206B: Advanced Geographic Information Systems (Same as Public Policy M224A), Professors Kawano and Brozen.
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.

### Winter 2011

• Biomathematics
• Biomath 171: Applied 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 M207B: Applied Genetic Modeling, Professor Sinsheimer.
Methods of computer-oriented human genetic analysis. Topics include statistical methodology underlying genetic analysis of both quantitative and qualitative complex traits. Laboratory for hands-on computer analysis of genetic data; laboratory reports required.
• Biomath 265A: Data Analysis Strategies I, Professor Karlamangla.
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 M270: Optimal Parameter Estimation and Experiment Design for Biomedical Systems, Staff.
Estimation methodology and model parameter estimation algorithms for fitting dynamic system models to biomedical data. Model discrimination methods. Theory and algorithms for designing optimal experiments for developing and quantifying models, with special focus on optimal sampling schedule design for kinetic models. Exploration of PC software for model building and optimal experiment design via applications in physiology and pharmacology.

• Biostatistics
• Biostat 100A: Introduction to Biostatistics, Professor Sinha.
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 100B: Introduction to Biostatistics, Professor Brookmeyer.
Introduction to analysis of variance, linear regression, and correlation analysis.
• Biostat 110B: Basic Biostatistics, Professor Telesca.
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 M237: Applied Genetic Modeling (Also listed as Biomath M270), Professor Sinsheimer.
Methods of computer-oriented human genetic analysis. Topics include statistical methodology underlying genetic analysis of both quantitative and qualitative complex traits. Laboratory for hands-on computer analysis of genetic data; laboratory reports required
• Biostat 250B: Linear Statistical Models, Professor Wong.
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 288: Seminar: Statistics in AIDS, Professor Sugar.
Recent statistical developments in analysis of AIDS data. Participants or outside speakers present their own research or discuss articles from literature.
• Biostat 411: Analysis of Correlated Data, Professor Weiss.
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, Professor Rojas.
Introduction to probability and statistics for economists, with emphasis on rigorous arguments.
• Econ 103: Introduction to Econometrics, Professor Rashidian.
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, Professors Hahn.
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 231B: System Models, Professor Hahn.
Multivariate regression, errors-in-variables, simultaneous equations, identification, proxy variables, latent variables, factor analysis of panel data, asymptotic distribution theory.

• 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 211C: Advanced Item Response Theory, Professor Cai.
• Educ 222B: Participant-observation Field Methods, Professor Erickson.
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: Linear Statistical Models in Social Science Research, Professor Webb.
Solid and comprehensive training in regression-based methods for analyzing quantitative social science data.
• Educ 255C: Seminar, Data Analysis, Professor Seltzer.
Focus on potential outcomes framework for causal inference. Use of various design and analysis strategies (e.g., propensity score matching) for drawing causal inferences in settings in which random assignment is not possible.

• 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.
• Epidem 410: Management of Epidemiologic Data, Professor Cochran
Data management for various epidemiologic study designs, confidentiality concerns; data management systems; introduction to mainframe computer.

• Health Services
• Hlt Ser 214: Measurements of Effectiveness and Outcomes of Healthcare, Professors Hays.
Historical perspective for development of health status measures and their utilization in assessment of outcomes and effectiveness in medical care. Review of current methods in context of current research and practice.

• Management
• Mgmt 264B: Regression with Applications in Marketing and Finance, Professor Rossi.

• Mechanical and Aerospace Engineering
• MechAE 174: Probability and Its Applications to Risk, Reliability, and Quality Control, Staff.
Introduction to probability theory; random variables, distributions, functions of random variables, models of failure of components, reliability, redundancy, complex systems, stress-strength models, fault tree analysis, statistical quality control by variables and by attributes, acceptance sampling.
• MechAE 271B: Stochastic Estimation, Staff.
Linear and nonlinear estimation theory, orthogonal projection lemma, Bayesian filtering theory, conditional mean and risk estimators.

• Nursing
• Nursing 203A: Basic Statistics and Fundamentals for Analysis, Professor Woo.
Introduction to applied statistics, including design, analysis of variance, correlation techniques, and regression. Sample size calculations, parametric versus nonparametric tests, and concepts of database design, management using statistical package programs.
• Nursing 204: Research Design and Technique, Professors Mentes and Thomas.
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.
• Nursing 208: Research in Nursing, Professor Williams.
Advanced discussions of psychosocial, behavioral, and biophysical measurement and analysis in nursing research. Analysis of psychometrics, reliability, and internal validity of research instruments in relation to outcomes in nursing research.

• 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 Bawn.
Applications of multiple regression in political science.
• Pol Sci 200D: Quantitative Methods in Politics, Professor Lewis.
Focus on logical and mathematical structure underlying some statistical methods that are frequently used in political science. Emphasis on understanding structure of the models rather than on gaining added experience using them to analyze data. Applied data analysis.
• Pol Sci M208D: Multivariate Analysis with Latent Variables, 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, 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 M144:  Measurement and Its Applications, 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.
• Psych 151: Research Methods in Health Psychology, Professor Schetter.
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, Professor Lu.
Advanced experimental design and planning of investigations.
• Psych M257: Multivariate Analysis with Latent Variables, 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.
• Psych 258: Special Problems in Psychological Statistics, Professor Krull.
Random coefficient models for analysis of data from individuals nested within groups and repeated observations of individuals (longitudinal growth models).  Fundamental principles of multivariate statistical analysis with emphasis on problems in social sciences. Topics include data screening, multivariate analysis of variance and covariance, discriminant analysis, canonical correlation analysis, principal components analysis, and factor analysis. Problem oriented with minimal emphasis on theory.

• Public Policy
• Pub Plc 208: Statistical Methods of Policy Analysis II, Professors Jensen and Moorthy.
Quantitative studies of public policy, covering regression analysis and its application to public policy questions.
• Pub Plc M244A: Introduction to Geographic Information Systems, Professors Estrada, Kolodziejczak, Brozen, and Kawano.
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 planning problem.

• Social Welfare
• Soc Wlf 285D:  Research in Child Welfare, Professor Jackson.
Integrated examination of development of empirical research in child welfare field. Critical assessment of current approaches to meet needs of children who come to attention of child welfare agencies. Examination of research and theory in child welfare field. Review of student knowledge of research methods and statistics.
• Soc Wlf 286B: Advanced Research Methods, Staff.
Advanced concepts underlying research methods. Continuing study of theoretical and conceptual approaches to research problem formulation; research design, including experimental, comparative, and survey; sampling; statistical methods; methods of observation and techniques of data analysis.
• Sociology
• Sociol 113: Statistical and Computer Methods for Social Research, Professor Grannis.
Continuation of Statistics 10, 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 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: Quantitative Data Analysis, Professor Brand.
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 10: Introduction to Statistical Reasoning, Professors Lew and Almohalvas.
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 Dinov.
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 101B: Introduction to Data Analysis and Regression, 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 102B: Introduction to Computation and Optimization for Statistics, Professor Sanchez.
Introduction to computational methods and optimization useful for statisticians. Use of computer programming to solve statistical problems. Topics include vector/matrix computation, multivariate normal distribution, principal component analysis, clustering analysis, gradient-based optimization, EM algorithm for missing data, and dynamic programming.
• 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 112: Statistical Methods for the Social Sciences, Professors Gould and Cetinkaya.
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 M154: Measurement and Its Applications (Also listed 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 C155: Applied Sampling, Professor Cochran.
Topics include methods of sampling from finite populations, sources of sampling and estimation bias, and methods of generating efficient and precise estimates of population characteristics. Practical applications of sampling methods via lectures and hands-on laboratory exercises.
• Stats C173: 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 186: Careers in Statistics, Professor Gould.
Discussion of applications of statistics by weekly guest speakers. How statistics is applied to legal questions, economic decisions, arts, environment, and other fields, with some emphasis on career paths in statistics.
• Stats 201B: Regression Analysis: Model Building, Fitting, and Criticism, Professor Hansen.
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: Matrix Algebra and Optimization, Professor DeLeeuw.
Survey of computational methods that are especially useful for statistical analysis, with implementations in statistical package R. Topics include matrix analysis, multivariate regression, principal component analysis, multivariate analysis, and deterministic optimization methods.
• Stats M232A: Statistial 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 M242: Multivariate Analysis with Latent Variables (Also listed as Psych 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.
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• Urban Planning
• Urbn Pl M206A: Introduction to Geographic Information Systems (Same as Public Policy M224A), Professors Estrada, Kolodziejczak, Brozen, and Kawano..
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, Pierce, and Pfeiffer.
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 2010

• Biomathematics
• Biomath 210: Optimization Methods in Biology, Professor Lange.
Modern computational biology relies heavily on finite-dimensional optimization. Survey of theory and numerical methods for discrete and continuous optimization, with applications from genetics, medical imaging, pharmacokinetics, and statistics.

• 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 Brookmeyer.
Basic concepts of statistical analysis applied to biological sciences. Topics include random variables, sampling distributions, parameter estimates, statistical inference.
• Biostat 200A: Biostatistics, Professors Sugar and Crespi-Chun.
Topics in methodology of applied statistics, such as design, analysis of variance, regression.
• Biostat 213: Statistical Simulation Techniques, Professor Telesca
Techniques for simulating important statistical distributions, with applications in biostatistics.
• Biostat 215: Survival Analysis, Professor Li
Statistical methods for analysis of survival data.
• Biostat 238: Methodology of Clinical Trials, Professor Wong
Methodological principles of clinical trials, actual practice and principles of trials. Considerable focus on phase two trials and multiclinical phase three trials. Emphasis on major inferential issues.
• Biostat 250A: Linear Statistical Models, Professor Cumberland.
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 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 M273: Classification and Regression Trees (CART) and other algorithms, Professor Kitchen.
Instruction in use of statistical tools in analysis of large datasets. Classification and regression trees as well as other adaptive algorithms. Implementation of CART software and other programs to real datasets.
• 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 M213: Research in Community and Patient Health Education, Professor Morisky.
Application of conceptual, theoretical, and evaluation skills to community-based health education risk-reduction programs. Computer applications, data management, and research methodologies taught through microcomputer and mainframe computer management and analysis of program databases.
• Com Hlth M218: Questionnaire Design and Administration, Professor Bourque.
Designing, testing, field use, and administration of data collection instruments, with particular emphasis on questionnaires.

• Economics
• Econ 41: Statistics for Economists, Professor Bognar.
Introduction to probability and statistics for economists, with emphasis on rigorous arguments.
• Econ 103: Introduction to Econometrics, Staff.
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, Professor Matzkin.
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 M231A: Econometrics: Single Equation Models, Professor Matzkin.
Linear regression model, specification error, functional form, autocorrelation, nonlinear estimation, distributed lags, nonnormality, univariate time series, qualitative dependent variables, aggregation, structural change.

• Education
• Educ 230A: Introduction to Research Design and Statistics, Professor Webb.
Key concepts and issues in design and conduct of social sciences research. Introduction to descriptive statistics and fundamentals of statistical inference.
• Educ 231A: Toolkit for Quantitative Methods Research, Professor Cai.
Elementary probability. Working knowledge with calculus. Mathematical and statistical results useful for advanced quantitative methodology research. Matrix algebra. Random vectors. Multivariate distribution theory. Likelihood and Bayesian estimation and inference. Linear and generalized linear models.
• Educ 231E: Statistical Analysis with Latent Variables, Professor Cai.
Extends path analysis (causal modeling) by considering models with measurement errors and multiple indicators of latent variables. Confirmatory factor analysis, covariance structure modeling, and multiple-group analysis. Identification, estimation, testing, and model building considerations.

• 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.
• Epidemiology M218: Questionnaire Design and Administration (Same as Com Hlth M218), Professor Bourque.
Designing, testing, field use, and administration of data collection instruments, with particular emphasis on questionnaires.
• Epidemiology 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.

• Human Complex Systems
• Hum CS M100: Formal Modeling and Simulations in Social Sciences (same as Anthropology M186 and Honors Collegium M150), Professor Read.
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, Sarin, Barz, Bikhchandani, and Mamer.
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 Doerling.
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.

• 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 250A: Advanced Psychological Statistics, Professor Lu.
Basic statistical techniques as applied to design and interpretation of experimental and observational research.
• Psych 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 203: Statistical Methods of Policy Analysis I, Professors Phillips and Kuo.
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 Rossman.
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: Quantitative Data Analysis, Professor Brand.
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 10: Introduction to Statistical Reasoning, Professor Gould.
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 Christou.
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 100A: Introduction to Probability, Professors Sanchez, Wu, and Christou.
Probability distributions, random variables, vectors, and expectation.
• Stats 101A: Introduction to Design and Analysis of Experiment, Professor Esfandiari.
Fundamentals of collecting data, including components of experiments, randomization and blocking, completely randomized design and ANOVA, multiple comparisons, power and sample size, and block designs.
• Stats 102A: Introduction to Computational Statistics with R, Professor DeLeeuw.
Introduction to programming and data analysis in R.
• Stats 112: Statistical Methods for Social Sciences, Staff.
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 130: Getting up to speed with SPSS, Stata, SAS, and R, Professor Lew.
Study of four commonly employed solutions -- SPSS (Statistical Package for Social Sciences), Stata, SAS (Statistical Analysis System), and R -- for data analytic and statistical issues in health sciences, engineering, economics, and government. Emphasis on applied problem solving, measurement issues in data analysis, use of computer for analysis of large-scale data.
• Stats 180: Introduction to Bayesian Statistics, Professor Sanchez.
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 vary according to interests of students.
• Stats 200A: Applied Probability, Professor Wu.
Simulation, renewal theory, martingale, and selected topics from queuing, reliability, speech recognition, computational biology, mathematical finance, epidemiology.
• Stats 201A: Research Design, Sampling, and Data Management, Professor Xu.
Conditioning, Markov chains, Poisson process, Brownian motion, stationary processes, applications.
• Stats 202A: Statistics Programming, Professor Hansen.
Topics include programming environments/languages such as UNIX, UNIX shell, Python, R, and Processing and data technologies/formats such as relational databases/SQL and XML, with emphasis on complex data types, including large collections of textual data, GPS traces, network logs, and various online sources.
• Stats M231: Pattern Recognition and Machine Learning, Professor Zsu.
Fundamental concepts, theories, and algorithms for pattern recognition and machine learning that are used in computer vision, image processing, speech recognition, data mining, statistics, and computational biology. Topics include Bayesian decision theory, parametric and nonparametric learning, clustering, complexity (VC-dimension, MDL, AIC), PCA/ICA/TCA, MDS, SVM, boosting.
• 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.
• Stats M244: Statistical Analysis with Latent Variables (Same as Education M231E), Professor Cai.
Extends path analysis (causal modeling) by considering models with measurement errors and multiple indicators of latent variables. Confirmatory factor analysis, covariance structure modeling, and multiple-group analysis. Identification, estimation, testing, and model building considerations.
• Stats M254: Statistical Methods in Computational Biology.
Introduction to statistical methods developed and widely applied in several branches of computational biology, such as gene expression, sequence alignment, motif discovery, comparative genomics, and biological networks, with emphasis on understanding of basic statistical concepts and use of statistical inference to solve biological problems.

• 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.

Summer 2010

• Biostatistics
• Biostat 100A: Introduction to Biostatistics, Professor Sayre.
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.

• Economics
• Econ 41: Statistics for Economists, Professors Park and Shi.
Introduction to probability and statistics for economists, with emphasis on rigorous arguments.

• Health Services
• Health Services 237A: Special Topics in Health Services Research Methodology, Professors Mangione and Ong.
Approaches to conceptualization, modeling, design, literature reviews, sampling, data collection, and research.

• Psychology
• Psych 100A: Psychological Statistics, Professors Ainsworth and Dehardt.
Basic statistical procedures and their application to research and practice in various areas of psychology.
• Psych 100B: Research Methods in Psychology, Professors Geiselman and 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.

• Sociology
• Sociol 20: Introduction to Sociological Research Methods, Professor Feinstein.
Introduction to methods used in contemporary sociological research, with focus on issues of research design, data collection, and analysis of data.
• Sociol 113: Statistical and Computer Methods for Social Research, Professor Feinstein.
Continuation of Statistics 10, 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.

• Statistics
• Stat 10: Introduction to Statistical Reasoning, Professors Sanchez and Cetinkaya.
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.
• Stat 12: Introduction to Statistical Methods for Geography and Environmental Studies, Professor Christou.
Introduction to statistical thinking and understanding, with emphasis on techniques used in geography and environmental science. Underlying logic behind statistical procedures, role of variation in statistical thinking, strengths and limitations of statistical summaries, and fundamental inferential tools. Emphasis on applications in geography and environmental science in laboratory work using professional statistical analysis package, including spatial statistics.
• Stat 13: Introduction to Statistical Methods for Life and Health Sciences, Professor Esfandiari.
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.
• Stat 100A: Introduction to Probability, Professor Christou.
Probability distributions, random variables, vectors, and expectation.
• Stat 100B: Introduction to Mathematical Statistics, Professor Sanchez.
Survey sampling, estimation, testing, data summary, one- and two-sample problems.
• Stat 112: Statistical Methods for 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.
• Stat 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.

Spring 2010

• 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 258: Introduction to Clinical Trials, Professor Frank.
Introduction to basic principles of good clinical trial design, trial implementation, and analysis.
• Biomath 265B: Data Analysis Strategies II, Staff
Continuation of course 265A; use of SAS computer language.
• Biomath 266: Advanced Biostatistics, Staff
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 Dabrowska.
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 M220: Advanced Experimental Statistics, Professors Garfinkel and JOhnston.
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 276: Inferential Techniques That Use Simulation, Professor Telesca.
Theory and application of recently developed techniques for statistical inference that use computer simulation. Topics include bootstrap, multiple imputation, data augmentation, stochastic relaxation, and sampling/importance resampling algorithm.
• Biostat M403B: 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.
• 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 Bognar.
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 142: Probabilistic Microeconomics, Staff.
Combination of basic probability introduced in Statistics 11 with microeconomic models presented in courses 11 and 101 in order to explain phenomena such as insurance, job search, and stock market behavior. Optimal production and consumption under uncertainty. Review of probability and introduction to alternative measures of risk and risk aversion.
• Econ 203C: Systems Models, Professor Hahn and Professor Ben-Moshe.
Multivariate regression, simultaneous equation estimation, identification, and latent variables.

• 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 230C: Linear Statistical Models in Social Science Research: Analysis of Designed Experiments, Professor Martinez-Fernandez.
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.
Solid and comprehensive training in experimental design and analysis methods, especially use of analysis of variance methods.

• Epidemiology
• Epidem 200C: Methods III: Analysis, Professors Greenland and Arah.
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 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 Rimoin.
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, Professors Needleman and Barnes.
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 201B: Econometrics and Business Forecasting, Professor Nickelsburg.
Development of standard topics in applied econometric modeling. Emphasis on assumptions underlying classical normal linear regression model, special problems in application, and interpretation of results. Practical applications extensively developed in student projects.
• Mgmt 239C: Empirical Research in Finance, Professor Roll.
In-depth study of empirical research in field of finance, statistical methodologies applied to test market efficiency, and asset pricing theory.
• 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, Professor 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, Professors Groseclose and O'Neill.
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 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.
• Psych 259: Quantitative Methods in Cognitive Psychology, Professors Lu and Liu.
Number of nonstatistical mathematical methods and techniques commonly used in cognitive psychology. Topics include Markov chains, other stochastic processes, queueing theory, information theory, frequency analysis, etc.

• Public Policy
• Pub Plc M224B: Advanced Geographic Information Systems, Professors Wang and Brozen.
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).

• Statistics
• Stats 10: Introduction to Statistical Reasoning, Professors Davis and Gould.
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 12: Introduction to Statistical Methods for Geography and Environmental Studies, Professor Christou.
Introduction to statistical thinking and understanding, with emphasis on techniques used in geography and environmental science. Underlying logic behind statistical procedures, role of variation in statistical thinking, strengths and limitations of statistical summaries, and fundamental inferential tools. Emphasis on applications in geography and environmental science in laboratory work using professional statistical analysis package, including spatial statistics.
• Stats 13: Introduction to Statistical Methods for Life and Health Sciences, Professor Christou.
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: Linear Models, Professor Sanchez.
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 Schoenberg.
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: Introduction to Monte Carlo Methods, 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.
• Stats 105: Statistics for Engineers, Professor Nesbitt.
Foundation of basic concepts and techniques of statistics. Topics include sampling distributions, statistical estimation (including maximum likelihood estimation), statistical intervals, and hypothesis testing, with emphasis on application of these concepts. Discussion of methods for checking whether assumptions required for mathematical foundations are appropriate for given set of data.
• Stats 112: Statistical Methods for the Social Sciences, Professor Sugano.
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 157: Probability and Statistics Data Modeling and Analysis Using SOCR, Professor Dinov.
Probability and statistics topics in data-driven and interactive manner using open Internet resources. Varieties of data, study-designs, and applications arising from biomedical, research, and simulated data to prepare students for innovative multidisciplinary research. Use of Statistics Online Computational Resource (SOCR).
• Stats C180/C236: Introduction to Bayesian Statistics, Professor Sanchez.
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 vary according to interests of students.
• 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 Yuille.
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 246: Statistical Model Selection, Staff.
Modern methods for constructing and evaluating statistical models, including non-Bayesian and Bayesian statistical modeling approaches. Discussion of theoretical parts and data analysis.
• Urban Planning
• Urbn Pl M206B: Advanced Geographic Information Systems (Same as Public Policy M224A), Professor Wang and Brozen.
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.

### Winter 2010

• Biomathematics
• Biomath 171: Applied 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 265A: Data Analysis Strategies I, Professor Karlamangla.
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).

• Biostatistics
• Biostat 100A: Introduction to Biostatistics, Professor Sinha.
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 100B: Introduction to Biostatistics, Professor Sugar.
Introduction to analysis of variance, linear regression, and correlation analysis.
• Biostat 110B: Basic Biostatistics, Professor Telesca.
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 M215: Survival Analysis, 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 M238:  Methodology of Clinical Trials, Professor Li.
Methodological principles of clinical trials, actual practice and principles of trials. Considerable focus on phase two trials and multiclinical phase three trials. Emphasis on major inferential issues.
• 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 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 (Same as Epidem 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.

• Economics
• Econ 41: Statistics for Economists, Professor Bognar.
Introduction to probability and statistics for economists, with emphasis on rigorous arguments.
• Econ 103: Introduction to Econometrics, Professor Casanova Rivas.
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, Professors Hahn.
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 231B: System Models, Professor Hahn.
Multivariate regression, errors-in-variables, simultaneous equations, identification, proxy variables, latent variables, factor analysis of panel data, asymptotic distribution theory.

• 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 222B: Participant-observation Field Methods, Professor Erickson.
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: Linear Statistical Models in Social Science Research, Professor Webb.
Solid and comprehensive training in regression-based methods for analyzing quantitative social science data.
• Educ 231B: Factor Analysis, Professor Cai.
Exploratory factor analysis, rotations, confirmatory factor analysis, multiple-group analysis.

• 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.
• Epidem 410: Management of Epidemiologic Data, Professor Cochran
Data management for various epidemiologic study designs, confidentiality concerns; data management systems; introduction to mainframe computer.

• Health Services
• Hlt Ser 214: Measurements of Effectiveness and Outcomes of Healthcare, Professors Hays and Cunningham.
Historical perspective for development of health status measures and their utilization in assessment of outcomes and effectiveness in medical care. Review of current methods in context of current research and practice.
• Hlt Ser 237B: Special Topics in Health Services: Research Methodology, Professor Zimmerman.
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.

• Management
• Mgmt 237E: Empirical Methods in Finance, Professor Lustig.
Econometric and statistical techniques commonly used in quantitative finance. Use of estimation application software in exercises to estimate volatility, correlations, stability, regressions, and statistical inference using financial time series.
• Mgmt 239C: Empirical Research in Finance, Professor Lustig.
In-depth study of empirical research in field of finance, statistical methodologies applied to test market efficiency, and asset pricing theory.

• Mechanical and Aerospace Engineering
• MechAE 174: Probability and Its Applications to Risk, Reliability, and Quality Control, Staff.
Introduction to probability theory; random variables, distributions, functions of random variables, models of failure of components, reliability, redundancy, complex systems, stress-strength models, fault tree analysis, statistical quality control by variables and by attributes, acceptance sampling.
• MechAE 271B: Stochastic Estimation, Professor Speyer.
Linear and nonlinear estimation theory, orthogonal projection lemma, Bayesian filtering theory, conditional mean and risk estimators.

• Nursing
• Nursing 173: Introduction to Research, Professor Macey.
Introduction to planning research project based on simple question. Specific components of research activities analyzed: specific aims and study purposes, variable definition, sample selection, data collection tools, data analyses, and ethical conduct in research studies. Critique of research reports.
• Nursing 208: Research in Nursing, Professor Evangelista.
Advanced discussions of psychosocial, behavioral, and biophysical measurement and analysis in nursing research. Analysis of psychometrics, reliability, and internal validity of research instruments in relation to outcomes in nursing research.

• 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 Bawn.
Applications of multiple regression in political science.
• Pol Sci 200D: Quantitative Methods in Politics, Professor Lewis.
Focus on logical and mathematical structure underlying some statistical methods that are frequently used in political science. Emphasis on understanding structure of the models rather than on gaining added experience using them to analyze data. Applied data analysis.
• Pol Sci M208D: Multivariate Analysis with Latent Variables, 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.
• Pol Sci 209: Special Topics in Formal Theory and Quantitative Methods, Professors Chwe and Lohmann.
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 M144:  Measurement and Its Applications, 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.
• Psych 151: 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 M238:  Survey Research Techniques in Psychocultural Studies, Professor Huo.
Techniques for conceptualizing, conducting, and analyzing survey data; instruction in qualitative strategies for enhancing survey research on psychocultural problems.
• Psych 250B: Advanced Psychological Statistics, Professor Nandy.
Advanced experimental design and planning of investigations.
• Psych 254A: Computing Methods for Psychology, Professor Lu.
Use of MATLAB, but only basic programming knowledge assumed; no prior knowledge of MATLAB required. Designed to teach basic computer methods relevant to work in experimental psychology and cognitive science. Topics include simulation/modeling, statistical data analysis, and stimulus presentation.
• Psych M257: Multivariate Analysis with Latent Variables, 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.
• Psych 258: Special Problems in Psychological Statistics, Professor Krull.
Random coefficient models for analysis of data from individuals nested within groups and repeated observations of individuals (longitudinal growth models).  Fundamental principles of multivariate statistical analysis with emphasis on problems in social sciences. Topics include data screening, multivariate analysis of variance and covariance, discriminant analysis, canonical correlation analysis, principal components analysis, and factor analysis. Problem oriented with minimal emphasis on theory.

• Public Policy
• Pub Plc 208: Statistical Methods of Policy Analysis II, Professor Jensen.
Quantitative studies of public policy, covering regression analysis and its application to public policy questions.
• Pub Plc M244A: Introduction to Geographic Information Systems, 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 planning problem.

• Social Welfare
• Soc Wlf 285D:  Research in Child Welfare, Professor Jackson.
Integrated examination of development of empirical research in child welfare field. Critical assessment of current approaches to meet needs of children who come to attention of child welfare agencies. Examination of research and theory in child welfare field. Review of student knowledge of research methods and statistics.
• Soc Wlf 286B: Advanced Research Methods, Staff.
Advanced concepts underlying research methods. Continuing study of theoretical and conceptual approaches to research problem formulation; research design, including experimental, comparative, and survey; sampling; statistical methods; methods of observation and techniques of data analysis.
• Sociology
• Sociol 113: Statistical and Computer Methods for Social Research, Professor Davis.
Continuation of Statistics 10, 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 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.

• Statistics
• Stats 10: Introduction to Statistical Reasoning, Professor Davis.
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 Dinov.
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 101B: Introduction to Data Analysis and Regression, 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 102B: Introduction to Computation and Optimization for Statistics, Professor Zhou.
Introduction to computational methods and optimization useful for statisticians. Use of computer programming to solve statistical problems. Topics include vector/matrix computation, multivariate normal distribution, principal component analysis, clustering analysis, gradient-based optimization, EM algorithm for missing data, and dynamic programming.
• 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 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 M154: Measurement and Its Applications (Also listed 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 C155: Applied Sampling, Professor Cochran.
Topics include methods of sampling from finite populations, sources of sampling and estimation bias, and methods of generating efficient and precise estimates of population characteristics. Practical applications of sampling methods via lectures and hands-on laboratory exercises.
• Stats C173: 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 186: Careers in Statistics, Professor Gould.
Discussion of applications of statistics by weekly guest speakers. How statistics is applied to legal questions, economic decisions, arts, environment, and other fields, with some emphasis on career paths in statistics.
• 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: Matrix Algebra and Optimization, Professor DeLeeuw.
Survey of computational methods that are especially useful for statistical analysis, with implementations in statistical package R. Topics include matrix analysis, multivariate regression, principal component analysis, multivariate analysis, and deterministic optimization methods.
• Stats M232A: Statistial 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 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 M242: Multivariate Analysis with Latent Variables (Also listed as Psych 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 M254: Statistical Methods in Computational Biology, Professor Zhou.
Introduction to statistical methods developed and widely applied in several branches of computational biology, such as gene expression, sequence alignment, motif discovery, comparative genomics, and biological networks, with emphasis on understanding of basic statistical concepts and use of statistical inference to solve biological problems.
•
• Urban Planning
• Urbn Pl M206A: Introduction to Geographic Information Systems (Same as Public Policy M224A), Professors Estrada, Firestine, and Asher.
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, Professor Liggett.
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 2009

• Biomathematics
• Biomath M207A: Theoretical Genetic Modeling, Professor Sinsheimer.
Mathematical models in statistical genetics. Topics include population genetics, genetic epidemiology, gene mapping, design of genetics experiments, DNA sequence analysis, and molecular phylogeny.
• Biomath 210: Optimization Methods in Biology, Professor Lange.
Modern computational biology relies heavily on finite-dimensional optimization. Survey of theory and numerical methods for discrete and continuous optimization, with applications from genetics, medical imaging, pharmacokinetics, and statistics.

• 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 Elashoff.
Basic concepts of statistical analysis applied to biological sciences. Topics include random variables, sampling distributions, parameter estimates, statistical inference.
• Biostat 200A: Biostatistics, Professors Belin and Crespi-Chun.
Topics in methodology of applied statistics, such as design, analysis of variance, regression.
• Biostat 202: Theory of Regression Analysis, Professor Crespi-Chun.
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 212: Distribution Free Methods, Professor Kitchen.
Theory and application of distribution free methods in biostatistics.
• Biostat 250A: Linear Statistical Models, Professor Cumberland.
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 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 M272: Theoretical Genetic Modeling (Same as Biomath M207A), Professor Sinsheimer.
Mathematical models in statistical genetics. Topics include population genetics, genetic epidemiology, gene mapping, design of genetics experiments, DNA sequence analysis, and molecular phylogeny.
• Biostat M278: Statistical Analysis of DNA Microarray Data, Professors Horvath and Elashoff.
Mathematical models in statistical genetics. Topics include population genetics, genetic epidemiology, gene mapping, design of genetics experiments, DNA sequence analysis, and molecular phylogeny.
• 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 M213: Research in Community and Patient Health Education, Professor Morisky.
Application of conceptual, theoretical, and evaluation skills to community-based health education risk-reduction programs. Computer applications, data management, and research methodologies taught through microcomputer and mainframe computer management and analysis of program databases.
• Com Hlth M218: Questionnaire Design and Administration, Professor Bourque.
Designing, testing, field use, and administration of data collection instruments, with particular emphasis on questionnaires.
• Com Hlth 219: Theory-Based Data Analysis, Professor Aneshensel.
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, Professor Brown.
Introduction to probability and statistics for economists, with emphasis on rigorous arguments.
• Econ 142: Probabilistic Microeconomics, Professor Bognar.
Optimal production and consumption under uncertainty. Review of probability and introduction to alternative measures of risk and risk aversion.
• Econ 203A: Probability and Statistics for Econometrics, Professor Matzkin.
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 M231A: Econometrics: Single Equation Models, Professor Matzkin.
Linear regression model, specification error, functional form, autocorrelation, nonlinear estimation, distributed lags, nonnormality, univariate time series, qualitative dependent variables, aggregation, structural change.

• Education
• Educ 230A: Introduction to Research Design and Statistics, Professor Webb.
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 Cai.
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.

• 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.
• Epidemiology M218: Questionnaire Design and Administration (Same as Com Hlth M218), Professor Bourque.
Designing, testing, field use, and administration of data collection instruments, with particular emphasis on questionnaires.
• Epidemiology 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.

• Human Complex Systems
• Hum CS M100: Formal Modeling and Simulations in Social Sciences (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 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 402: Data and Decisions, Professors McCardle, Sarin, Barz, Bikhchandani, and Mamer.
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 Sarna.
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 6R: Introduction to Data Analysis, Professor Zaller.
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 comparative politics.
• Pol Sci 200A: Statistical Methods I, Professor Denardo.
Introduction to statistical analysis of political data.

• Psychology
• Psych 100A: Psychological Statistics, Professors McAuliffe and Nandy.
Basic statistical procedures and their application to research and practice in various areas of psychology.
• Psych 100B: Research Methods in Psychology, Professor Bjork.
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 151: Research Methods in Health Psychology, Professor Robles.
Research methods used in health psychology, including experimental, quasi-experimental, and nonexperimental methods. Examples and projects from health psychology.
• Psych 250A: Advanced Psychological Statistics, Professor Reise.
Basic statistical techniques as applied to design and interpretation of experimental and observational research.

• 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 Rossman.
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 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.

• Statistics
• Stats 10: Introduction to Statistical Reasoning, Professors Davis and Gould.
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 Christou.
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 35B: Introduction to Probability with Applications to Poker, Professor Paik Schoenberg.
Exploration of some main topics in introductory probability theory, especially discrete probability problems, that are useful in wide variety of scientific applications. Topics include conditional probability and conditional expectation, combinatorics, laws of large numbers, central limit theorem, Bayes theorem, univariate distributions, Markov processes, and Brownian motion. Examination of computer simulation in depth and discussion of computational approximations of solutions to complex problems using R, with examples of situations and concepts that arise naturally when playing Texas Hold'em and other games.
• Stats 100A: Introduction to Probability, Professors Sanchez, Wu, and Christou.
Probability distributions, random variables, vectors, and expectation.
• Stats 101A: Introduction to Design and Analysis of Experiment, Professor Esfandiari.
Fundamentals of collecting data, including components of experiments, randomization and blocking, completely randomized design and ANOVA, multiple comparisons, power and sample size, and block designs.
• Stats 102A: Introduction to Computational Statistics with R, Professor Sanchez.
Introduction to programming and data analysis in R.
• 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 200A: Applied Probability, Professor Wu.
Simulation, renewal theory, martingale, and selected topics from queuing, reliability, speech recognition, computational biology, mathematical finance, epidemiology.
• Stats 201A: Research Design, Sampling, and Data Management, Professor Xu.
Conditioning, Markov chains, Poisson process, Brownian motion, stationary processes, applications.
• Stats 202A: Statistics Programming, Professor Hansen.
Topics include programming environments/languages such as UNIX, UNIX shell, Python, R, and Processing and data technologies/formats such as relational databases/SQL and XML, with emphasis on complex data types, including large collections of textual data, GPS traces, network logs, and various online sources.
• 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 M231: Pattern Recognition and Machine Learning, Professor Zsu.
Fundamental concepts, theories, and algorithms for pattern recognition and machine learning that are used in computer vision, image processing, speech recognition, data mining, statistics, and computational biology. Topics include Bayesian decision theory, parametric and nonparametric learning, clustering, complexity (VC-dimension, MDL, AIC), PCA/ICA/TCA, MDS, SVM, boosting.
• 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.

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