Course names are links to course syllabus or course website.
Undergraduate Courses
Anthropology • Economics • Education • Earth Science •
Political Science • Psychology • Public Policy • Statistics
Graduate Courses
Anthropology • Biological Sciences • Economics • Education •
Health and Research Policy • International Policy Studies • Management Econ •
Petroleum Engineering • Political Science • Psychology • Public Policy • Sociology •
Statistics
| Number | Name | Instructor, Quarter, and Description |
| anthsci 149/ anthsci 208 | Models and Imaging in Archaeological Computing | John Rick Winter 2005-2006 Hands-on archaeo-logical field research in the local area. The practical working methodology of the archaeologist through excavation and site survey with training in registration preservation and analysis of archaeological data. |
| anthsci 192 | Data Anaysis in the Anthropolgical Sciences | Ian Robertson and James Jones Winter 2005-2006 Univariate multivariate and graphical methods used for analyzing quantitative data in anthropological research. Archaeological and paleobiological examples. Recommended: algebra. Uses R. |
| econ 102a | Introduction to Statistical Methods (Postcalculus) for Social Scientists | Derek Stimel Winter 2005-2006 Description and examples of the use of statistical techniques relevant to economics. Basic rules of probability conditional probability discrete and continuous probability distributions. Point estimation tests of hypotheses confidence intervals and linear regression model. Prerequisite: MATH 41 or equivalent. Uses excel |
| econ 102b | Introduction to Econometrics | Ryu Keun-Kwan Spring 2005-2006 Descriptive statistics. Regression analysis. Hypothesis testing. Analysis of variance. Heteroskedasticity serial correlation errors in variables simultaneous equations. Prerequisites: 50 102A or equivalent. Recommended: computer experience. |
| econ 102c | Advanced Topics in Econometrics | Luigi Pistaferri not given 2005-2006 Identification and estimation of the effect of human capital variables on earnings (e.g. the return to education tenure) and identification and estimation of labor supply models focusing on microeconomic data. Topics: instrumental variable estimation limited dependent variable models (probit logit and tobit models) and panel data techniques (fixed effect and random effect models dynamic panel data models). |
| econ 103 | Applied Econometrics | Orazio Attanasio Spring 2005-2006 The construction and use of econometric models for analyzing economic phenomena. Students complete individual projects and core material. Topics vary with the instructor. Limited enrollment. Prerequisites: 52 102B. |
| econ 170/270 | Intermediate Econometrics I | Hansen Mahajan Autumn 2005-2006 Probability random variables and distributions; large sample theory; theory of estimation and hypothesis testing. Limited enrollment. Prerequisites: math and probability at the level of Chapter 2 Paul G. Hoel Introduction to Mathematical Statistics 5th ed. |
| econ 171/271 | Intermediate Econometrics II | Frank Wolak Autumn 2005-2006 Linear regression model relaxation of classical-regression assumptions simultaneous equation models linear time series analysis. Limited enrollment. Prerequisite: 270. |
| econ 172/272 | Intermediate Econometrics III | Thomas MaCurdy Winter 2005-2006 Continuation of 271. Nonlinear estimation qualitative response models limited dependent variable (Tobit) models. Limited enrollment. Prerequisite: 271. |
| educ 150 | Introduction to Data Analysis and Interpretation | Ann Porteus Winter 2005-2006 Primarily for master's students with little or no experience. Focus is on reading literature and interpreting descriptive and inferential statistics especially those commonly found in education. Topics: basic research design instrument reliability and validity description statistics correlation t-tests simple analysis of variance simple and multiple regression and contingency analysis. |
| educ 160 | Introduction to Statistical Methods in Education | Rich Shavelson Autumn 2005-2006 For doctoral students with little or no prior statistics. Organization of data descriptive statistics elementary methods of inference hypothesis testing and confidence intervals. Computer package used. Students cannot also receive credit for PSYCH 60 or for STATS 60/160. (all areas) |
| ges 160 | Statistical Methods for Earth and Environmental Sciences: General Introduction | Paul Switzer Autumn 2005-2006 Extracting information from data using statistical summaries and graphical visualization statistical measures of association and correlation distribution models sampling error estimation and confidence intervals linear models and regression analysis introduction to time-series and spatial data with geostatistics applications including environmental monitoring natural hazards and experimental design. Either or both of 160 and 161 may be taken. |
| polisci150b/350b | Political Methodology II | Simon Jackman Winter 05-06 Understanding and using the linear regression model in a social-science context: properties of the least squares estimator; inference and hypothesis testing; assessing model fit; presenting results for publication; consequences and diagnosis of departures from model assumptions; outliers and influential observations graphical techniques for model fitting and checking; interactions among exploratory variables; pooling data; extensions for binary responses. GER:DB-Math |
| polisci 150a/350a | Political Methodology I | Douglas Rivers Autumn 05-06 Introduction to probability and statistical inference with applications to political science and public policy. Prerequisite: elementary calculus. GER:DB-Math |
| polisci 150c/350c | Political Methodology III | Douglas Rivers, Jonathan Wand Spring 05-06 Models for discrete outcomes time series measurement error and simultaneity. Introduction to nonlinear estimation large sample theory. Prerequisite: 150B/350B. |
| polisci 151b | Data Analysis for Political Science | Simon Jackman Spring 05-06 Operationalization of concepts measurement scale construction finding and pooling/ merging data cross-tabulations tests of association comparison of means correlation scatterplots and regression models. How to present the results of data analysis in research reports essays and theses. Emphasis is on getting and using data with appropriate statistical software. Prior mathematics not required. GER:DB-Math |
| polisci 152/352 | Introduction to Game Theoretic Methods in Political Science | James Fearon Winter 05-06 Concepts and tools of non-cooperative game theory developed using political science questions and applications. Formal treatment of Hobbes' theory of the state and major criticisms of it; examples from international politics. Primarily for graduate students; undergraduates admitted with consent of instructor. |
| psych 10/ Stats 60 | Introduction to Statistical Methods: Precalculus | Ewart Thomas Winter 05-06 Techniques for organizing data computing and interpreting measures of central tendency variability and association. Estimation confidence intervals tests of hypotheses t-tests correlation and regression. Possible topics: analysis of variance and chi-square tests computer statistical packages. |
| psych110 | Research Methods and Experimental Design | Mark Lepper Spring 05-06 Structured research exercises and design of an individual research project. Prerequisite: consent of instructor. GER:DB-SocSci WIM |
| pubpol 105 | Quantitative Methods and Their Applications to Public Policy | Geoffrey Rothwell Spring 05-06 Reviews material covered in prerequisites with applications of qualitative independent variable techniques to labor market data. Maximum likelihood estimation and qualitative dependent variable models with an application to voting models. Final papers estimate influence of quantitative and qualitative independent variables on Congressional voting probabilities. Prerequisites: ECON 102A B. GER:DB-SocSci |
| stats 30 | Statistical Thinking | Bradley Efron Not given 2005-06 Statistical inference with a minimum of mathematical formulation. Topics: comparisons and the randomized clinical trial statistical significance accuracy and the meaning of statistical error (plus or minus) correlation and regression to the mean exploratory methods and data mining life tables and survival analysis and learning from experience (Bayesian inference). Lectures supplemented with web-based statistical simulations. GER:DB-Math |
| stats 110 | Statistical Methods in Engineering and the Physical Sciences | Jay Lindley Bartroff, Karen Anita Kapur Summer 05-06 Introduction to statistics for engineers and physical scientists. Topics: descriptive statistics probability interval estimation tests of hypotheses nonparametric methods linear regression analysis of variance elementary experimental design. Prerequisite: one year of calculus. GER:DB-Math |
| stats 116 | Theory of Probability | Serban Nacu Spring 05-06 Probability spaces as models for phenomena with statistical regularity. Discrete spaces (binomial hypergeometric Poisson). Continuous spaces (normal exponential) and densities. Random variables expectation independence conditional probability. Introduction to the laws of large numbers and central limit theorem. Prerequisites: MATH 52 and familiarity with infinite series or equivalent. GER:DB-Math |
| stats 141 | Biostatistics | David Rogosa, Rudolf Sombillo AngelesAutumn 05-06Statistical analysis of biological data. Topics: discrete and continuous distributions testing hypotheses and confidence procedures fixed and random effects analysis of variance regression and correlation. Wilcoxon and other nonparametric procedures inference on contingency tables and other data arising from counts. Tests of goodness of fit. Emphasis is on finding numerical solutions to biostatistical problems and practical interpretations and their implications. GER:DB-Math |
| stats 191 | Introduction to Applied Statistics | Raja Velu Winter 05-06 Statistical tools for modern data analysis. Topics include regression and prediction elements of the analysis of variance bootstrap and cross-validation. Emphasis is on conceptual rather than theoretical understanding. Applications to social/biological sciences. Student assignments/projects require use of the software package R. Recommended: 60 110 or 141. GER:DB-Math |
| Number | Name | Instructor, Quarter, and Description |
| anthsci 149/ anthsci 208 | Models and Imaging in Archaeological Computing | John Rick Winter 2005-2006 Hands-on archaeo-logical field research in the local area. The practical working methodology of the archaeologist through excavation and site survey with training in registration preservation and analysis of archaeological data. |
| biomedin 251 | Outcome Analysis | Jay Bhattacharya Spring 2005-2006 Introduction to methods of conducting empirical studies which use large existing medical survey and other databases to ask both clinical and policy questions. Econometric and statistical models used to conduct medical outcomes research. How research is conducted on medical and health economics questions when a randomized trial is impossible. Problem sets emphasize hands-on data analysis and application of methods including re-analyses of well-known studies. Prerequisites: one or more courses in probability and statistics or biostatistics. Uses Stata |
| biomedin 303 | Statistics for Research | Michael Walker given 2006-2007 Statistical methods commonly used in research. Emphasis is on when and how to use the methods rather than on proofs. How to describe data and detect unusual values compare treatment effects interpret p-values detect and quantify trends detect and measure association and correlation determine the sample size and power for an experiment and choose statistical tests and software. Topics include descriptive statistics (mean median standard deviation standard error) probability paired and unpaired t-tests analysis of variance correlation regression chi-square discriminant analysis and power and sample size. Statistical analysis software including Excel and Statistica. |
| econ 170/270 | Intermediate Econometrics I | Hansen Mahajan Autumn 2005-2006 Probability random variables and distributions; large sample theory; theory of estimation and hypothesis testing. Limited enrollment. Prerequisites: math and probability at the level of Chapter 2 Paul G. Hoel Introduction to Mathematical Statistics 5th ed. |
| econ 171/271 | Intermediate Econometrics II | Frank Wolak Autumn 2005-2006 Linear regression model relaxation of classical-regression assumptions simultaneous equation models linear time series analysis. Limited enrollment. Prerequisite: 270. |
| econ 172/272 | Intermediate Econometrics III | Thomas MaCurdy Winter 2005-2006 Continuation of 271. Nonlinear estimation qualitative response models limited dependent variable (Tobit) models. Limited enrollment. Prerequisite: 271. |
| econ 273a | Advanced Econometrics I | Frank Wolak Winter 2005-2006 Parametric asymptotic theory. Large-sample properties of estimators defined as the solution to an optimization problem under a variety of assumptions for the true data generation process. General large sample results for maximum likelihood nonlinear least squares nonlinear instrumental variables estimators including the generalized method of moments estimator under general conditions. Asymptotic hypothesis testing procedures derived for each estimation framework. |
| econ 273b | Advanced Econometrics II | Aprajit Mahajan Spring 2005-2006 Simulations methods. Semiparametric and nonparametric methods. Optimal rate of convergence and semiparametric efficiency bounds. Prerequisite: 273A. |
| econ 274 | Limited Dependent Variables | Ryu Keun-Kwan Spring 2005-2006 Discrete choice models; Tobit models; Markov chain and duration models. Prerequisite: 273 or consent of instructor. |
| econ 275 | Time Series and Simultaneous Equation | Peter Hansen Winter 2005-2006 Stochastic processes in the time and frequency domain. Time and frequency domain estimation. Unit roots co-integration time-varying conditional second moment models instrumental variables estimation of dynamic models. |
| educ 250a | Statistical Analysis in Educational Research | Sean Reardon Winter 2005-2006 Primarily for doctoral students. Regression and categorical models as widely used data-analytic procedures. Topics: basic regression including multiple and curvilinear regression regression diagnostics analysis of residuals and model selection logistic regression analysis of categorical data. Proficiency with statistical computer packages. Prerequisite: 160 or equivalent. (all areas) |
| educ 250b | Statistical Analysis in Educational Research: Analysis of Variance | Rich Shavelson Spring 2005-2006 Primarily for doctoral students. Variance models as widely used data analytic procedures especially in experimental quasi-experimental and criterion-group designs. Topics: single-factor ANOVA the factorial between and within subjects and mixed design ANOVA (fixed random and mixed models) analysis of covariance multiple comparison procedures. Prerequisite: 160X or equivalent. (all areas) |
| educ 250c | Statistical Analysis in Educational Research: Applied Multivariate Analysis | Olkin Ingram Winter 2005-2006 Primarily for doctoral students in education social and behavioral sciences. Multivariate analysis of variance discriminant analysis factor analysis correlation analysis. Advanced regression methods. Data compression: principal components analysis clustering. Computer packages for data analysis. Prerequisites: 250B 257 STATS 200 or equivalent. (all areas) |
| educ 257a b | Statistical Methods for Behavioral and Social Sciences | David Rogosa not given 2005-2006 For students with experience in empirical research. Analysis of data from experimental studies through factorial designs randomized blocks repeated measures; regression methods through multiple regression model building analysis of covariance; categorical data analysis through log-linear models logistic regression. Integrated with the use of statistical computing packages. Prerequisite: analysis of variance and regression at the level of STATS 161. |
| educ 259 | Application of Hierarchical Linear Models in Behavioral and Social Research | Anthony Bryk Winter 2005-2006 The measurement of change and the assessment of multi-level effects or the unit of analysis problem. The inadequacy of traditional statistical techniques for the modeling of hierarchy. |
| educ 260x | Popular Advanced Statistical Methods | David Rogosa Spring 2005-2006 Methods for accommodating the nested structure of educational data such as students within classrooms within schools which arise as units of analysis problems ecological regression or hierarchical linear models. Methods for complex measurement models in regression settings known as structural equation models causal models covariance structures. See http://www.stanford.edu/class/ed260. |
| educ 257c/ Soc257 | Inference in Quantitative Educational and Social Science Research | Sean Reardon not given 2005-2006 Quantitative methods to make causal inferences in the absence of randomized experiment including the use of natural and quasi-experiments instrumental variables regression discontinuity matching estimators longitudinal methods fixed effects estimators and selection modeling. Assumptions implicit in these approaches and appropriateness in research situations. Students develop research proposals relying on these methods. Prerequisites: exposure to quantitative research methods; multivariate regression. |
| educ 351 | Design and Analysis of Longitudinal Research | David Rogosa Winter 2005-2006 The analysis of longitudinal data as central to empirical research on learning and development. Topics: growth models measurement of change reciprocal effects stability analysis of durations including survival analysis and experimental and non-experimental group comparisons. See http://www.stanford.edu/~rag/. Prerequisite: statistics at the level of 257. |
| hrp 259 | Introduction to Probability and Statistics for Epidemiology | Kristen Cobb Autumn 2005-2006 Topics: random variables expectation variance probability distributions the central limit theorem sampling theory hypothesis testing confidence intervals. Correlation regression analysis of variance and nonparametric tests. Introduction to least squares and maximum likelihood estimation. Emphasis is on medical applications. |
| hrp 262/ Stats 262 | Intermediate Biostatistics: Regression Prediction Survival Analysis | Kristen Cobb Spring 2005-2006 Methods for analyzing longitudinal data. Topics include Kaplan-Meier methods Cox regression hazard ratios time-dependent variables longitudinal data structures profile plots missing data modeling change MANOVA repeated-measures ANOVA GEE and mixed models. Emphasis is on practical applications. Prerequisites: basic ANOVA and linear regression. |
| ips201b | Applied Econometrics | Anne Royalty Spring 2005-2006 Econometric modeling techniques and applications. Theory including bivariate and multivariate regression analysis inference and hypothesis testing heteroscedasticity autocorrelation and simultaneous-equation models. |
| mgtecon603 | Econometric Methods I | Peter Reinhard Hansen Autumn 2005-2006 This course has the object of giving students basic concepts and abilities in econometrics including linear regressions of various types and the testing of certain types of hypotheses. The course emphasizes geometrically motivated methods such as orthogonal projection. Some examples for application will be chosen from economics. The prerequisite for this course is a strong degree of familiarity with statistics for example a good understanding of Mood Graybill and Boes' Introduction to the Theory of Statistics third edition (New York McGraw-Hill 1974). Students should therefore also be conversant with undergraduate calculus and linear algebra. |
| mgtecon604 | Econometric Methods II | Alan T. Sorensen or Kenneth J. Singleton Winter 2005-2006 This course presents a comprehensive treatment of econometric methods for linear models. Among the topics covered are: the classical linear regression model heteroskedasticity and lagged dependent variables linear simultaneous equations systems panel data dichotomous dependent variables and sample selection issues. Throughout maximum likelihood and instrumental variables estimation strategies and hypothesis testing procedures are discussed. |
| mgtecon605 | Econometric Methods III | Peter C. Reiss Spring 2005-2006 This course completes the first-year sequence in econometrics. The course initially develops the theoretical and practical aspects of maximum likelihood quasi-maximum likelihood GMM and non-linear estimators in greater detail. The instructor will then discuss how these methods are used in practice. Time permitting we will briefly consider more advanced topics and applications including: time series methods non-parametric estimators and simulation estimators. |
| peteng242/ ges 242 | Topics in Advanced Geostatistics | Andre Journel not given 2005-2006 Conditional expectation theory and projections in Hilbert spaces; parametric versus non-parametric geostatistics; Boolean Gaussian fractal indicator and annealing approaches to stochastic imaging; multiple point statistics inference and reproduction; neural net geostatistics; Bayesian methods for data integration; techniques for upscaling hydrodynamic properties. May be repeated for credit. Prerequisites: 240 advanced calculus C++/Fortran. |
| peteng245 / geophys 245 | Probability Theory | Albert Tarantola not given 2005-2006 Probabilistic formulations and solutions to inverse problems. Monte Carlo methods for solving inverse problems. Metropolis algorithm. Deterministic solutions using maximum likelihood gradient methods. Dealing with prior probability and data uncertainty. Gaussian and non-Gaussian model formulations. Application to earth science problems. Prerequisite: introduction to probability theory course. |
| peteng284 | Optimization: Deterministic and Stochastic Approaches | Roland N Horne Autumn 2005-2006 Deterministic and stochastic methods for optimization in earth sciences and engineering. Linear and nonlinear regression classification and pattern recognition using neural networks simulated annealing and genetic algorithms. Deterministic optimization using non-gradient-based methods (simplex) and gradient-based methods (conjugated gradient steepest descent Levenberg-Marquardt Gauss-Newton) eigen-value and singular value decomposition. Applications in petroleum engineering geostatistics and geophysics. Prerequisite: CME 200 (formerly ME 200A) or consent of instructor. |
| polisci150b/350b | Political Methodology II | Simon Jackman Winter 05-06 Understanding and using the linear regression model in a social-science context: properties of the least squares estimator; inference and hypothesis testing; assessing model fit; presenting results for publication; consequences and diagnosis of departures from model assumptions; outliers and influential observations graphical techniques for model fitting and checking; interactions among exploratory variables; pooling data; extensions for binary responses. GER:DB-Math |
| polisci 150a/350a | Political Methodology I | Douglas Rivers Autumn 05-06 Introduction to probability and statistical inference with applications to political science and public policy. Prerequisite: elementary calculus. GER:DB-Math |
| polisci 150c/350c | Political Methodology III | Douglas Rivers, Jonathan Wand Spring 05-06 Models for discrete outcomes time series measurement error and simultaneity. Introduction to nonlinear estimation large sample theory. Prerequisite: 150B/350B. |
| polisci 152/352 | Introduction to Game Theoretic Methods in Political Science | James Fearon Winter 05-06 Concepts and tools of non-cooperative game theory developed using political science questions and applications. Formal treatment of Hobbes' theory of the state and major criticisms of it; examples from international politics. Primarily for graduate students; undergraduates admitted with consent of instructor. |
| polisci 353a/b/c | Workshop in Statistical Modeling | Simon Jackman, Douglas Rivers, Jonathan Wand Winter 05-06 Theoretical aspects and empirical applications of statistical modeling in the social sciences. Guest speakers. Students present a research paper. Prerequisite: 350B or equivalent. |
| polisci 355 | Advanced Topics in Research Methods | Jonanthan Wand Winter 05-06 Applications to American and comparative politics and international relations. |
| psych 253 | Statistical Theory Models and Methodology | Ewart Thomas Spring 05-06 Practical and theoretical advanced data analytic techniques such as loglinear models signal detection meta-analysis logistic regression reliability theory and factor analysis. Prerequisite: 252 or EDUC 257. |
| psych252 | Statistical Methods for Behavioral and Social Sciences | Ewart Thomas Autumn 05-06 For students who seek experience and advanced training in empirical research. Analysis of data from experimental through factorial designs randomized blocks repeated measures; regression methods through multiple regression model building analysis of covariance; categorical data analysis through two-way tables. Integrated with the use of statistical computing packages. Prerequisite: 10 or equivalent. |
| soc 381a | Sociological Methods 1A: Computer-Assisted Data Analysis | Sean Farley Everton Autumn 05-06 The computer as research tool. Common data sets in the social sciences. Necessary skills for further courses in sociological methodology. |
| soc 382 | Sociological Methodology II: The General Linear Model | Nancy Tuma Winter 05-06 The general linear model for discrete and continuous variables. Introduction to model selection the principles of estimation assessment of fit and modeling diagnostics. Prerequisites: 281A B or equivalents. |
| soc 383 | Sociological Methodology III: Advanced Models for Discrete Outcomes | Nancy Tuma Spring 05-06 Required for Ph.D. in Sociology. The rationale for and interpretation of static and dynamic models for the analysis of discrete variables. Prerequisites: 281A B and 382 or equivalent. |
| soc 387 | Frontiers of Quantitative Sociological Research | Nancy Tuma Not given 2005-06 Advanced topics in quantitative sociological research especially recently-developed models and methods. Possible topics: robust regression methods boot-strapping local likelihood estimation quantile regression two-sided logit models event count models event sequence models heterogeneous diffusion models and models for change in social networks. |
| soc 388 | Log-Linear Models | Michael Rosenfeld Autumn 05-06 Analysis of categorical data with log-linear and negative binomial models. Measures of fit and hypothesis testing. |
| stats 200 | Introduction to Statistical Inference | Joseph Romano Winter 05-06 Modern statistical concepts and procedures derived from a mathematical framework. Statistical inference decision theory; point and interval estimation tests of hypotheses; Neyman-Pearson theory. Bayesian analysis; maximum likelihood large sample theory. Prerequisite: 116. |
| stats 202 | Data Analysis | Jerome Friedman, Victoria Clare Stodden Autumn 05-06 Data mining is used to discover patterns and relationships in data. Emphasis is on large complex data sets such as those in very large databases or through web mining. Topics: decision trees neural networks association rules clustering case based methods and data visualization. |
| stats 203 | Introduction to Regression Models and Analysis of Variance | Paul Switzer Spring 05-06 Modeling and interpretation of observational and experimental data using linear and nonlinear regression methods. Model building and selection methods. Multivariable analysis. Fixed and random effects models. Experimental design. Pre- or corequisite: 200 |
| stats 205 | Introduction to Nonparametric Statistics | Sadri Khalessi Winter 05-06 Nonparametric analogs of the one- and two-sample t tests and analysis of variance; the sign test median test Wilcoxon's tests and the Kruskal-Wallis and Friedman tests tests of independence. Nonparametric regression and nonparametric density estimation modern nonparametric techniques nonparametric confidence interval estimates. |
| stats 206 | Applied Multivariate Analysis | Art Owen Winter 05-06 Introduction to the statistical analysis of several quantitative measurements on each observational unit. Emphasis is on concepts computer-intensive methods. Examples from economics education geology psychology. Topics: multiple regression multivariate analysis of variance principal components factor analysis canonical correlations multidimensional scaling clustering. |
| stats 207 | Introduction to Time Series Analysis | Spring 05-06 Time series models used in economics and engineering. Trend fitting autoregressive and moving average models and spectral analysis Kalman filtering and state-space models. Seasonality transformations and introduction to financial time series. Prerequisite: basic course in Statistics at the level of 200. |
| stats 208 | Introduction to the Bootstrap | Susan Holmes Spring 05-06 The bootstrap is a computer-based method for assigning measures of accuracy to statistical estimates. By substituting computation in place of mathematical formulas it permits the statistical analysis of complicated estimators. Topics: nonparametric assessment of standard errors biases and confidence Statistics school of humanities and sciences intervals; related resampling methods including the jackknife cross-validation and permutation tests. Theory and applications. Prerequisite: course in statistics or probability. |
| stats 211/educ493b | Topics in Quantitative Methods: Meta-Analysis | Ingram Olkin Winter 05-06 Meta-analysis as a quantitative method for combining the results of independent studies enabling researchers to evaluate available evidence. Examples of meta-analysis in medicine education and social and behavioral sciences. Statistical methods include nonparametric methods contingency tables regression and analysis of variance and Bayesian methods. Project involving an existing published meta-analysis. Prerequisite: basic sequence in statistics. |
| stats 212 | Applied Statistics with SAS | Victoria Clare Stodden Summer 05-06 Data analysis and implementation of statistical tools in SAS. Topics: reading in and describing data categorical data dates and longitudinal data correlation and regression nonparametric comparisons ANOVA multiple regression multivariate data analysis using arrays and macros in SAS. Prerequisite: statistical techniques at the level of 191 or 203; knowledge of SAS not required. |
| stats 214 | Randomness in the Physical World | Susan Holmes Alternate years given 2006-07 Topics include: random numbers and their generation and application; disordered systems quenching and annealing; percolation and fractal structures; universality the renormalization group and limit theorems; path integrals partition functions and Wiener measure; random matrices; and optical estimation. Prerequisite: introductory course in statistical mechanics or analysis. |
| stats 227 | Statistical Computing | Susan Holmes Not given 2005-06 Numerical aspects of least squares nonlinear and robust regression. Eigenvector-eigenvalue computations and analyses. Monte Carlo methods: generation of uniformly distributed random numbers generation of special distributions variance reduction techniques. The complexity of algorithms used in statistics: sorting computation of quantiles nearest neighbor search fast Fourier transform. Prerequisites: statistics at the level of 200 matrix algebra programming language. |
| stats 237 | Time Series Modeling and Forecasting | Jerry Shan Summer 05-06 Box-Jenkins and Bayesian approaches. State-space and change-point models. Application to revenue prediction forecasting product demand and other real world problems. Development and assessment of models and forecasts in practical applications. Hands-on experience with real data. |
| stats 239a/b | Workshop in Quantitative Finance | Valdo Durrleman Autumn 05-06 Topics of current interest. |
| stats 240 | Statistical Methods in Finance | Tze Lai Spring 05-06 Regression analysis and applications to the Capital Asset Pricing Model and multifactor pricing models. Principal components and multivariate analysis. Smoothing techniques and estimation of yield curves. Statistical methods for financial time series; value at risk. Term structure models and fixed income research. Estimation and modeling of volatilities. Hands-on experience with financial data. |
| stats 252 | Data Mining and Electronic Business | Andreas Sebastian Weigend Summer 05-06 The Internet and related technologies have caused the cost of communication and transactions to plummet and consequently the amount of potentially relevant data to explode. The underlying principles statistical issues and algorithmic approaches to data mining and e-business with real world examples. |
| stats 253 | Spatial Statistics | Paul Switzer Not given 2005-06 Statistical descriptions of spatial variability spatial random functions grid models spatial partitions spatial sampling linear and nonlinear interpolation and smoothing with error estimation Bayes methods and pattern simulation from posterior distributions multivariate spatial statistics spatial classification nonstationary spatial statistics space-time statistics and estimation of time trends from monitoring data spatial point patterns models of attraction and repulsion. Applications to earth and environmental sciences meteorology astronomy remote-sensing ecology materials. GER:DB-Math |
| stats 260(a b c) | Workshop in Biostatistics | Richard Olshen Spring 05-06 Applications of statistical techniques to current problems in medical science. Enrollment for more than 2 units of credit involves extra reading or consulting and requires consent of instructor. |
| stats 261/ HRP 261/biomedin 233 | Intermediate Biostatistics: Analysis of Discrete Data | Trevor Hastie, Kristen Cobb Winter 05-06 The 2x2 table. Chi-square test. Fisher's exact test. Odds ratios. Sampling plans; case control and cohort studies. Series of 2x2 tables. Mantel Hantzel. Other tests. k x m tables. Matched data logistic models. Conditional logistic analysis application to case-control data. Log-linear models. Generalized estimating equations for longitudinal data. Cell phones and car crashes: the crossover design. Special topics: generalized additive models classification trees bootstrap inference. |
| stats 270/370 | A Course in Bayesian Statistics | Not given 2005-06 Statistics--(Ph.D. students register for 370.) Bayesian statistics including theory applications and computational tools. Topics: history of Bayesian methods foundational problems (what is probability?) subjective probability and coherence exchangeability and deFinetti's theorem. Conjugate priors Laplace approximations Gibbs sampling hierarchical and empirical Bayes nonparametric methods Dirichlet and Polya tree priors. Bayes robustness asymptotic properties of Bayes procedures. |
| stats 300 | Advanced Topics in Statistics | Joseph Romano Summer 05-06 |
| stats 305 | Introduction to Statistical Modeling | Art Owen, Elizabeth Anne Purdom Autumn 05-06 The linear model: simple linear regression polynomial regression multiple regression anova models; and with some extensions orthogonal series regression wavelets radial basis functions and MARS. Topics: normal theory inference (tests confidence intervals power) related distributions (t chi-square F) numerical methods (QR SVD) model selection/regularization (Cp AIC BIC) diagnostics of model inadequacy and remedies including bootstrap inference and cross-validation. Emphasis is on problem sets involving substantial computations with data sets including developing extensions of existing methods. Prerequisite: consent of instructor 116 200 one applied statistics course CS 106A MATH 114. |
| stats 306A | Methods for Applied Statistics | Art Owen Winter 05-06 Extension of modeling techniques of 305: binary and discrete response data and nonlinear least squares. Topics include regression Poisson loglinear models classification methods clustering. May be repeated for credit. Prerequisite: 305 or equivalent. |
| stats 314 | Advanced Statistical Methods | Joseph Romano Spring 05-06 Topic this year is multiple hypothesis testing. The demand for new methodology for the simultaneous testing of many hypotheses as driven by modern applications in genomics imaging astronomy and finance. High dimensionality: how tests of many hypotheses may be considered simultaneously. Classical techniques and recent developments. Stepwise methods generalized error rates such as the false discovery rate and the role of resampling. May be repeated for credit. |
| stats 315A | Modern Applied Statistics: Learning | Trevor Hastie Winter 05-06 Two-part sequence on new techniques for predictive and descriptive learning using ideas that bridge gaps among statistics computer science and artificial intelligence. Emphasis is on statistical aspects of their application and integration with more standard statistical methodology. Predictive learning refers to estimating models from data with the goal of predicting future outcomes in particular regression and classification models. Descriptive learning is used to discover general patterns and relationships in data without a specific predictive goal. From a statistical perspective it can be viewed as computer automated exploratory analysis of usually large complex data sets. |
| stats 315B | Modern Applied Statistics: Data Mining | Jerome Friedman Spring 05-06 Two-part sequence on new techniques for predictive and descriptive learning using ideas that bridge gaps among statistics computer science and artificial intelligence. Emphasis is on statistical aspects of their application and integration with more standard statistical methodology. Predictive learning refers to estimating models from data with the goal of predicting future outcomes in particular regression and classification models. Descriptive learning is used to discover general patterns and relationships in data without a specific predictive goal. From a statistical perspective it can be viewed as computer automated exploratory analysis of usually large complex data sets. |
| stats 316/math 236 | Introduction to Stochastic Differential Equations | George Papanicolaou Winter 05-06 Brownian motion stochastic integrals and diffusions as solutions of stochastic differential equations. Functionals of diffusions and their connection with partial differential equations. Random walk approximation of diffusions. Prerequisite: 136 or equivalent and differential equations. |
| stats 317 | Stochastic Processes | Not given 2005-06 Processes--Semimartingales stochastic integration Ito's formula Girsanov's theorem. Gaussian and related processes. Stationary/isotropic processes. Integral geometry and geometric probability. Maxima of random fields and applications to spatial statistics and imaging. |
| stats 318 | Modern Markov Chains | Persi Diaconis Yiyuan She Autumn 05-06 Tools for understanding Markov chains as they arise in applications. Random walk on graphs reversible Markov chains Metropolis algorithm Gibbs sampler hybrid Monte Carlo auxiliary variables hit and run Swedson-Wong algorithms geometric theory Poincare-Nash-Cheger-Log-Sobolov inequalities. Comparison techniques coupling stationary times Harris recurrence central limit theorems and large deviations. |
| stats 324 | Classical Multivariate and Random Matrix Theory | Persi Diaconis Not given 2005-06 Properties of multivariate normal Wishart t and beta distributions as they arise in statistical problems. Distribution of eigenvalues and vectors of classical ensembles. Marchenko-Pasteur and Tracy-Widom distributions. Determinental random fields with applications in statistics combinatorics and physics. |
| stats 362 | Monte Carlo Sampling | Art Owen Autumn 05-06 Sampling--Fundamentals of Monte Carlo methods. Generating uniform and nonuniform variables random vectors and processes. Monte Carlo integration and variance reduction. Quasi-Monte Carlo sampling. Markov chain Monte Carlo including Gibbs sampling and Metropolis-Hastings. Examples problems and motivations from Bayesian statistics computational finance computer graphics physics. |