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# MAPSS - Courses

# Quantitative Undergraduate, Graduate Courses

**Undergraduate Courses**

Economics • Education • Management Economics • Political Science • Psychology • Sociology • Statistics • School of Medicine • Anthropology •

**Graduate Courses**

Economics • Education • Health and Research Policy • Management Economics • Petroleum Engineering • Political Science • Psychology • Sociology • Statistics • Anthropology •

##### Undergraduate Courses

Number | Name | Instructor, Quarter, and Description | |||

## Economics | |||||

ECON 102c |
Advanced Topics in Econometrics
[ syllabus ] |
Luigi Pistaferri [ email ]
Spring 2008-2009
Identification and estimation of the effect of human capital variables on earnings (such as 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).
Software Package: none |
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ECON 102b |
Introduction to Econometrics
[ syllabus ] |
APRAJIT MAHAJAN [ email ]
Winter 2008-2009
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.
Software Package: none |
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ECON 171 |
Intermediate Econometrics II
[ syllabus ] |
Frank Wolak [ email ]
Autumn 2008-2009
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.
Software Package: none |
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ECON 270 |
Intermediate Econometrics I
[ syllabus ] |
Peter Hansen [ email ]
Autumn 2008-2009
(Same as ECON 170.) (Graduate students register for 270; see 270.) 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.
Software Package: none |
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ECON 172 |
Intermediate Econometrics III |
Staff
Spring 2008-2009
(Same as ECON 272.) (Graduate students register for 272.) Continuation of 271. Nonlinear estimation, qualitative response models, limited dependent variable (Tobit) models. Limited enrollment. Prerequisite: 271.
Software Package: none |
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ECON 50 |
Economic Analysis I |
Ran Abramitzky [ email ]
Autumn 2008-2009
Individual consumer and firm behavior under perfect competition. The role of markets and prices in a decentralized economy. Monopoly in partial equilibrium. Economic tools developed from multivariable calculus using partial differentiation and techniques for constrained and unconstrained optimization. Prerequisites: 1 or 1A and MATH 51. GER:DB-Math
Software Package: |
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ECON 51 |
Economic Analysis II |
Liran Einav [ email ]
Winter 2008-2009
Neoclassical analysis of general equilibrium, welfare economics, imperfect competition, externalities and public goods, intertemporal choice and asset markets, risk and uncertainty, game theory, adverse selection, and moral hazard. Multivariable calculus is used. Prerequisite: 50.
Software Package: |
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ECON 102A |
Introduction to Statistical Methods (Postcalculus) for Social Scientists |
Faye Steiner [ email ]
Autumn 2007-2008
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. GER:DB-Math
Software Package: |
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ECON 272 |
Intermediate Econometrics III |
Staff
Spring 2008-2009
(Same as ECON 172.) (Graduate students register for 272.) Continuation of 271. Nonlinear estimation, qualitative response models, limited dependent variable (Tobit) models. Limited enrollment. Prerequisite: 271.
Software Package: none |
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## Education | |||||

EDUC 150 |
EDUC 150. Introduction to Data Analysis and Interpretation
[ syllabus ] |
Ann Porteus [ email ]
Autumn 2008-2009
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, one-way analysis of variance, and simple and multiple regression.
Software Package: none |
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EDUC 160 |
EDUC 160. Introduction to Statistical Methods in Education
[ syllabus ] |
David Rogosa [ email ]
Autumn 2008-2009
(Master\'s students register for 150.) 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)
Software Package: none |
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EDUC 401a |
EDUC 401A. Mini Courses in Methodology: Statistical Packages for the Social Sciences |
Staff
Autumn 2008-2009
Statistical analysis using SPSS, including generating descriptive statistics, drawing graphs, calculating correlation coefficients, conducting t-tests, analysis of variance, and linear regression. Building up datasets, preparing datasets for analysis, conducting statistical analysis, and interpreting results.
Software Package: |
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EDUC 251b |
EDUC 251B. Statistical Analysis in Educational Research: Analysis of Variance |
Rich Shavelson [ email ]
Spring 2008-2009
Primarily for doctoral students. ANOVA models as widely used data analytic procedures, especially in experimental, quasi-experimental, and criterion-group designs. Topics: single-factor ANOVA; factorial between and within subjects and mixed design ANOVA (fixed, random, and mixed models); analysis of covariance; and multiple comparison procedures. Prerequisite: 250A or equivalent. (all areas)
i am
Software Package: |
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## Management Economics | |||||

ME 112/21 |
Mathematical Programming and Combinatorial Optimization |
Amin Saberi [ email ]
Spring 0-1
(Graduate students register for 212; same as CME 208.) Combinatorial and mathematical programming (integer and non-linear) techniques for optimization. Topics: linear program duality and LP solvers; integer programming; combinatorial optimization problems on networks including minimum spanning trees, shortest paths, and network flows; matching and assignment problems; dynamic programming; linear approximations to convex programs; NP-completeness. Hands-on exercises. Prerequisites: CS 106A or X; ENGR 62 or MATH 103. GER:DB-EngrAppSci
Software Package: |
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ME 107/20 |
Interactive Management Science |
Sam Savage [ email ]
Autumn 0-1
—(Graduate students register for 207.) Analytical techniques such as linear and integer programming, Monte Carlo simulation, forecasting, decision analysis, and Markov chains in the environment of the spreadsheet. Materials include spreadsheet add-ins for implementing these and other techniques. Emphasis is on building intuition through interactive modeling, and extending the applicability of this type of analysis through integration with existing business data structures. Project required of those enrolled in 207. GER:DB-EngrAppSci
Software Package: |
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ME 206a |
ME 206A. Entrepreneurial Design for Extreme Affordability |
James Patell [ email ]
Winter 2008-2009
(Same as OIT 333.) Bass Seminar. Project course jointly offered by School of Engineering and Graduate School of Business. Students apply engineering and business skills to design product prototypes, distribution systems, and business plans for entrepreneurial ventures in developing countries for challenges faced by the world\'s poor. Topics include user empathy, appropriate technology design, rapid prototype engineering and testing, social technology entrepreneurship, business modeling, and project management. Weekly design reviews; final course presentation. Industry and adviser interaction. Limited enrollment via application; see http://extreme.stanford.edu.
Software Package: |
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## Political Science | |||||

POLISCI 350B |
POLISCI 350B. Political Methodology II
[ syllabus ] |
Douglas Rivers [ email ]
Winter 2008-2009
(Same as POLISCI 150B.) 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.
Software Package: R |
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POLISCI 150c |
POLISCI 150C. Political Methodology III |
Simon Jackman [ email ]
Spring 2008-2009
(Same as POLISCI 350C.) Models for discrete outcomes, time series, measurement error, and simultaneity. Introduction to nonlinear estimation, large sample theory. Prerequisite: 150B/350B.
Software Package: |
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POLISCI 352 |
POLISCI 352. Introduction to Game Theoretic Methods in Political Science
[ syllabus ] |
James Fearon [ email ]
Winter 2008-2009
(Same as POLISCI 152.) 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.
Software Package: |
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POLISCI 152 |
POLISCI 152. Introduction to Game Theoretic Methods in Political Science
[ syllabus ] |
James Fearon [ email ]
Winter 2008-2009
(Same as POLISCI 352.) 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.
Software Package: |
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POLISCI 150a |
POLISCI 150A. Political Methodology I
[ syllabus ] |
Jonathan Wand [ email ]
Autumn 2008-2009
(Same as POLISCI 350A.) Introduction to probability and statistical inference, with applications to political science and public policy. Prerequisite: elementary calculus. GER:DB-Math
Software Package: |
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POLISCI 241S |
Spatial Approaches to Social Science (ANTHRO 230D) |
Claudia Engel [ email ]
Winter 2011-2012
This multidisciplinary course combines different approaches to how GIS and spatial tools can be applied in social science research. We take a collaborative, project oriented approach to bring together technical expertise and substantive applications from several social science disciplines. The course aims to integrate tools, methods, and current debates in social science research and will enable students to engage in critical spatial research and a multidisciplinary dialogue around geographic space.
Software Package: |
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POLISCI 150b |
POLISCI 150B. Political Methodology II
[ syllabus ] |
Douglas Rivers [ email ]
Winter 2008-2009
(Same as POLISCI 350B.) 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
Software Package: R |
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## Psychology | |||||

PSYCH 253 |
Statistical Theory, Models, and Methodology |
Ewart Thomas [ email ]
Spring 2008-2009
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.
Software Package: |
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PSYCH 10 |
PSYCH 10. Introduction to Statistical Methods: Precalculus
[ syllabus ] |
Guenther Walther [ email ]
Winter 2008-2009
(Same as STATS 60, STATS 160.) 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. GER:DB-Math
Software Package: |
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## Sociology | |||||

SOC 381 |
SOC 381. Sociological Methodology I: Introduction |
Asaf Levanon [ email ]
Autumn 2008-2009
Enrollment limited to first-year Sociology doctoral students. Basic math and statistics. Types of variables, how to recode and transform variables, and how to manage different types of data sets. Introduction to statistical packages and programming.
Software Package: |
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## Statistics | |||||

STATS 241 |
STATS 241. Statistical Modeling in Financial Markets |
Tze Lai [ email ]
Spring 2008-2009
(SCPD students register for 241P.) Nonparametric regression and yield curve smoothing. Advanced time series modeling and forecasting. Market risk measures. Substantive and empirical modeling approaches in financial markets. Statistical trading strategies. Prerequisite: 240 or equivalent.
Software Package: |
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STATS 60 |
PSYCH 10. Introduction to Statistical Methods: Precalculus
[ syllabus ] |
Guenther Walther [ email ]
Winter 2008-2009
(Same as STATS 60, STATS 160.) 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. GER:DB-Math
Software Package: |
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## School of Medicine | |||||

MED 147 |
MED 147. Methods in Community Assessment, Evaluation, and Research |
Michaela Kiernan [ email ]
Winter 2008-2009
(Same as MED 247.) Development of pragmatic skills for design, implementation, and analysis of structured interviews, focus groups, survey questionnaires, and field observations. Topics include: principles of community-based participatory research, including importance of dissemination; strengths and limitations of different study designs; validity and reliability; construction of interview and focus group questions; techniques for moderating focus groups; content analysis of qualitative data; survey questionnaire design; and interpretation of commonly-used statistical analyses.
Software Package: |
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## Anthropology | |||||

ANTHSCI 116 |
Data Analysis for Quantitative Research |
Ian Robertson [ email ]
Spring 2011-2012
This course allows graduate and advanced undergraduate students in archaeology and anthropology to acquire practical skills in quantitative data analysis. Some familiarity with basic statistical methods is useful but not assumed; the structure of the course will be flexible enough to accommodate a range of student expertise and interests. Topics covered include: statistics and graphics in R; database design, resampling methods, diversity measures, contingency table analysis, and introductory methods in spatial analysis.
Software Package: |
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ANTHSCI 155 |
Research Methods in Ecological Anthropology |
Douglas Bird [ email ]
Spring 2011-2012
The course prepare students for the methodological and practical aspects of doing ecologically oriented, quantitative anthropological field research. The primary goal is to explore what it means to ask anthropological questions in a systematic way. We will focus on understanding what can constitute an interesting question, how to frame a question in way that facilitates investigation, and how to design methods to begin investigating a question. In turn, the course will provide a format to refine research projects in preparation for doing more extensive fieldwork.
Software Package: |
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ANTHSCI 130D |
Spatial Approaches to Social Science (ANTHRO 230D) |
Claudia Engel [ email ]
Winter 2011-2012
This multidisciplinary course combines different approaches to how GIS and spatial tools can be applied in social science research. We take a collaborative, project oriented approach to bring together technical expertise and substantive applications from several social science disciplines. The course aims to integrate tools, methods, and current debates in social science research and will enable students to engage in critical spatial research and a multidisciplinary dialogue around geographic space.
Software Package: |

##### Graduate or Cross Listed Courses

Number | Name | Instructor, Quarter, and Description | |||

## Economics | |||||

ECON 171 |
Intermediate Econometrics II
[ syllabus ] |
Frank Wolak [ email ]
Autumn 2008-2009
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.
Software Package: none |
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ECON 270 |
Intermediate Econometrics I
[ syllabus ] |
Peter Hansen [ email ]
Autumn 2008-2009
(Same as ECON 170.) (Graduate students register for 270; see 270.) 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.
Software Package: none |
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ECON 170 |
Intermediate Econometrics I
[ syllabus ] |
Peter Hansen [ email ]
Autumn 2008-2009
(Same as ECON 270.) (Graduate students register for 270; see 270.) 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.
Software Package: none |
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ECON 172 |
Intermediate Econometrics III |
Staff
Spring 2008-2009
(Same as ECON 272.) (Graduate students register for 272.) Continuation of 271. Nonlinear estimation, qualitative response models, limited dependent variable (Tobit) models. Limited enrollment. Prerequisite: 271.
Software Package: none |
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ECON 274 |
Advanced Econometrics II
[ syllabus ] |
Joseph Romano [ email ]
Winter 2008-2009
(Formerly 273B); Possible topics: nonparametric density estimation and regression analysis; sieve approximation; local polynomial regression; spline regression; cross validation; indirect inference; resampling methods: bootstrap and subsampling; quantile regression; nonstandard asymptotic distribution theory; empirical processes; set identification and inference.
Software Package: none |
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ECON 276 |
ECON 276. Limited Dependent Variables
[ syllabus ] |
Matthew C. Harding [ email ]
Spring 2008-2009
(Formerly 274.) Possible topics: discrete choice models; Tobit models; duration models; semiparametric methods; single index models; rank regression; U-statistics; bounds and incomplete models; linear and nonlinear static and dynamic treatment effects; local instrumental variables; matching; propensity score; inverse probability weighting; models with measurement errors and unobserved heterogeneity; stratified sampling. Discrete endogenous variables. Information theoretic alternative to gmm estimation. Nonlinear panel data. Prerequisite: 273 or consent of instructor.
Software Package: none |
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ECON 273 |
Advanced Econometrics I
[ syllabus ] |
Han Hong [ email ]
Autumn 2008-2009
Possible topics: parametric asymptotic theory. M and Z estimators. General large sample results for maximum likelihood; nonlinear least squares; and nonlinear instrumental variables estimators including the generalized method of moments estimator under general conditions. Model selection test. Consistent model selection criteria. Nonnested hypothesis testing. Markov chain Monte Carlo methods. Asymptotic hypothesis testing procedures derived for each estimation framework.
Software Package: none |
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ECON 272 |
Intermediate Econometrics III |
Staff
Spring 2008-2009
(Same as ECON 172.) (Graduate students register for 272.) Continuation of 271. Nonlinear estimation, qualitative response models, limited dependent variable (Tobit) models. Limited enrollment. Prerequisite: 271.
Software Package: none |
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## Education | |||||

EDUC 160 |
EDUC 160. Introduction to Statistical Methods in Education
[ syllabus ] |
David Rogosa [ email ]
Autumn 2008-2009
(Master\'s students register for 150.) 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)
Software Package: none |
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EDUC 250b |
Statistical Analysis in Educational Research: Analysis of Variance
[ syllabus ] |
Sean Reardon [ email ]
Winter 2008-2009
Primarily for doctoral students; part of doctoral research core; prerequisite for advanced statistical methods courses in School of Education. Basic regression, a widely used data-analytic procedure, including multiple and curvilinear regression, regression diagnostics, analysis of residuals and model selection, logistic regression. Proficiency with statistical computer packages.
Software Package: none |
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EDUC 250c |
Statistical Analysis in Educational Research: Applied Multivariate Analysis |
Shelley Goldman [ email ]
Winter 2008-2009
Primarily for doctoral students; part of doctoral research core. Methods for collecting and interpreting qualitative data including case study, ethnograpy, discourse analysis, observation, and interview.
Software Package: none |
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EDUC 260x |
Popular Advanced Statistical Methods
[ syllabus ] |
David Rogosa [ email ]
Winter 2008-2009
(Same as HRP 239, STATS 209.) Statistical modeling in experimental and non-experimental settings, including misconceptions in social science applications such as causal models. Text is Statistical Models: Theory and Practice, by David Freedman. See http://www-stat.stanford.edu/~rag/stat209. Prerequisite: intermediate-level statistical methods including multiple regression, logistic regression, and log-linear models.
Software Package: R |
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EDUC 401a |
EDUC 401A. Mini Courses in Methodology: Statistical Packages for the Social Sciences |
Staff
Autumn 2008-2009
Statistical analysis using SPSS, including generating descriptive statistics, drawing graphs, calculating correlation coefficients, conducting t-tests, analysis of variance, and linear regression. Building up datasets, preparing datasets for analysis, conducting statistical analysis, and interpreting results.
Software Package: |
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EDUC 251b |
EDUC 251B. Statistical Analysis in Educational Research: Analysis of Variance |
Rich Shavelson [ email ]
Spring 2008-2009
Primarily for doctoral students. ANOVA models as widely used data analytic procedures, especially in experimental, quasi-experimental, and criterion-group designs. Topics: single-factor ANOVA; factorial between and within subjects and mixed design ANOVA (fixed, random, and mixed models); analysis of covariance; and multiple comparison procedures. Prerequisite: 250A or equivalent. (all areas)
i am
Software Package: |
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EDUC 493 |
EDUC 493. Workshop in Design and Analysis of Non-Experimental Research |
David Rogosa [ email ]
Spring 2008-2009
For second-year and later students with data analysis or research design activities including in dissertation planning or analysis. Readings and exercises developed around participating student research. Topics may include: multilevel data analysis; usefulness of structural equation models (path analysis); and implementation of matching methods and regression adjustments for comparing non-equivalent groups. Various computing customs accommodated. See http://www-stat.stanford.edu/~rag/ed493/. Prerequisite: intermediate statistical methods course work.
Software Package: |
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## Health and Research Policy | |||||

HRP 261 |
Intermediate Biostatistics: Analysis of Discrete Data
[ syllabus ] |
K Sainani
Winter 2008-2009
(Same as BIOMEDIN 233, STATS 261.) Methods for analyzing data from case-control and cross-sectional studies: the 2x2 table, chi-square test, Fisher\'s exact test, odds ratios, Mantel-Haenzel methods, stratification, tests for matched data, logistic regression, conditional logistic regression. Emphasis is on data analysis in SAS. Special topics: cross-fold validation and bootstrap inference.
Software Package: |
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HRP 259 |
HRP 259. Introduction to Probability and Statistics for Epidemiology
[ syllabus ] |
K Sainani
Autumn 2008-2009
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.
Software Package: |
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HRP 262 |
Intermediate Biostatistics: Regression Prediction Survival Analysis
[ syllabus ] |
K Sainani
Spring 2008-2009
(Same as STATS 262.) 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.
Software Package: |
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HRP 258 |
HRP 258. Introduction to Probability and Statistics for Clinical Research |
K Sainani
Spring 2008-2009
Open to medical and graduate students; required of medical students in the Clinical Research Scholarly Concentration. Tools to evaluate medical literature. Topics include random variables, expectation, variance, probability distributions, the central limit theorem, sampling theory, hypothesis testing, confidence intervals, correlation, regression, analysis of variance, and survival analysis.
Software Package: |
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## Management Economics | |||||

ME 112/21 |
Mathematical Programming and Combinatorial Optimization |
Amin Saberi [ email ]
Spring 0-1
(Graduate students register for 212; same as CME 208.) Combinatorial and mathematical programming (integer and non-linear) techniques for optimization. Topics: linear program duality and LP solvers; integer programming; combinatorial optimization problems on networks including minimum spanning trees, shortest paths, and network flows; matching and assignment problems; dynamic programming; linear approximations to convex programs; NP-completeness. Hands-on exercises. Prerequisites: CS 106A or X; ENGR 62 or MATH 103. GER:DB-EngrAppSci
Software Package: |
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ME 107/20 |
Interactive Management Science |
Sam Savage [ email ]
Autumn 0-1
—(Graduate students register for 207.) Analytical techniques such as linear and integer programming, Monte Carlo simulation, forecasting, decision analysis, and Markov chains in the environment of the spreadsheet. Materials include spreadsheet add-ins for implementing these and other techniques. Emphasis is on building intuition through interactive modeling, and extending the applicability of this type of analysis through integration with existing business data structures. Project required of those enrolled in 207. GER:DB-EngrAppSci
Software Package: |
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ME 603 |
Econometric Methods I
[ syllabus ] |
Peter Hansen [ email ]
Autumn 2007-2008
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.
Software Package: |
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ME 206a |
ME 206A. Entrepreneurial Design for Extreme Affordability |
James Patell [ email ]
Winter 2008-2009
(Same as OIT 333.) Bass Seminar. Project course jointly offered by School of Engineering and Graduate School of Business. Students apply engineering and business skills to design product prototypes, distribution systems, and business plans for entrepreneurial ventures in developing countries for challenges faced by the world\'s poor. Topics include user empathy, appropriate technology design, rapid prototype engineering and testing, social technology entrepreneurship, business modeling, and project management. Weekly design reviews; final course presentation. Industry and adviser interaction. Limited enrollment via application; see http://extreme.stanford.edu.
Software Package: |
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ME 605 |
Econometric Methods III
[ syllabus ] |
Peter Reiss [ email ]
Spring 2007-2008
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.
Software Package: |
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ME 604 |
Econometric Methods II
[ syllabus ] |
Alan Sorensen [ email ]
Winter 2007-2008
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.
Software Package: |
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## Petroleum Engineering | |||||

PE 284 |
Optimization: Deterministic and Stochastic Approaches
[ syllabus ] |
Ronald Horne [ email ]
Autumn 2007-2008
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.
Software Package: |
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## Political Science | |||||

POLISCI 402 |
Methods of Analysis Program in the Social Sciences (MAPSS) Workshop |
Simon D. Jackman [ email ]
Autumn 2008-2009
(Same as COMM 310.) Colloquium series. Creation and application of new methodological techniques for social science research. Presentations on methodologies of use for social scientists across departments at Stanford by guest speakers from Stanford and elsewhere. See http://mapss.stanford.edu. May be repeated for credit.
Software Package: |
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POLISCI 353a |
POLISCI 353A. Workshop in Statistical Modeling |
Jonathan Wand [ email ]
Autumn 2008-2009
Theoretical aspects and empirical applications of statistical modeling in the social sciences. Guest speakers. Students present a research paper. Prerequisite: 350B or equivalent.
Software Package: |
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POLISCI 350B |
POLISCI 350B. Political Methodology II
[ syllabus ] |
Douglas Rivers [ email ]
Winter 2008-2009
(Same as POLISCI 150B.) 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.
Software Package: R |
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POLISCI 150c |
POLISCI 150C. Political Methodology III |
Simon Jackman [ email ]
Spring 2008-2009
(Same as POLISCI 350C.) Models for discrete outcomes, time series, measurement error, and simultaneity. Introduction to nonlinear estimation, large sample theory. Prerequisite: 150B/350B.
Software Package: |
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POLISCI 352 |
POLISCI 352. Introduction to Game Theoretic Methods in Political Science
[ syllabus ] |
James Fearon [ email ]
Winter 2008-2009
(Same as POLISCI 152.) 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.
Software Package: |
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POLISCI 152 |
POLISCI 152. Introduction to Game Theoretic Methods in Political Science
[ syllabus ] |
James Fearon [ email ]
Winter 2008-2009
(Same as POLISCI 352.) 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.
Software Package: |
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POLISCI 150a |
POLISCI 150A. Political Methodology I
[ syllabus ] |
Jonathan Wand [ email ]
Autumn 2008-2009
(Same as POLISCI 350A.) Introduction to probability and statistical inference, with applications to political science and public policy. Prerequisite: elementary calculus. GER:DB-Math
Software Package: |
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POLISCI 241S |
Spatial Approaches to Social Science (ANTHRO 230D) |
Claudia Engel [ email ]
Winter 2011-2012
This multidisciplinary course combines different approaches to how GIS and spatial tools can be applied in social science research. We take a collaborative, project oriented approach to bring together technical expertise and substantive applications from several social science disciplines. The course aims to integrate tools, methods, and current debates in social science research and will enable students to engage in critical spatial research and a multidisciplinary dialogue around geographic space.
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POLISCI 150b |
POLISCI 150B. Political Methodology II
[ syllabus ] |
Douglas Rivers [ email ]
Winter 2008-2009
(Same as POLISCI 350B.) 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
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## Psychology | |||||

PSYCH 253 |
Statistical Theory, Models, and Methodology |
Ewart Thomas [ email ]
Spring 2008-2009
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.
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PSYCH 253 |
Statistical Theory Models and Methodology |
Ewart Thomas [ email ]
Spring 2008-2009
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.
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PSYCH 252 |
Statistical Methods for Behavioral and Social Sciences
[ syllabus ] |
Ewart Thomas [ email ]
Autumn 2008-2009
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.
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## Sociology | |||||

SOC 381 |
SOC 381. Sociological Methodology I: Introduction |
Asaf Levanon [ email ]
Autumn 2008-2009
Enrollment limited to first-year Sociology doctoral students. Basic math and statistics. Types of variables, how to recode and transform variables, and how to manage different types of data sets. Introduction to statistical packages and programming.
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## Statistics | |||||

STATS 206 |
STATS 206. Applied Multivariate Analysis |
Sadri Khalessi [ email ]
Autumn 2008-2009
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. Pre- or corequisite: 200.
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STATS 209 |
Popular Advanced Statistical Methods
[ syllabus ] |
David Rogosa [ email ]
Winter 2008-2009
(Same as HRP 239, STATS 209.) Statistical modeling in experimental and non-experimental settings, including misconceptions in social science applications such as causal models. Text is Statistical Models: Theory and Practice, by David Freedman. See http://www-stat.stanford.edu/~rag/stat209. Prerequisite: intermediate-level statistical methods including multiple regression, logistic regression, and log-linear models.
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STATS 241 |
STATS 241. Statistical Modeling in Financial Markets |
Tze Lai [ email ]
Spring 2008-2009
(SCPD students register for 241P.) Nonparametric regression and yield curve smoothing. Advanced time series modeling and forecasting. Market risk measures. Substantive and empirical modeling approaches in financial markets. Statistical trading strategies. Prerequisite: 240 or equivalent.
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STATS 253 |
STATS 253. Spatial Statistics |
J Taylor
Spring 2008-2009
(Same as STATS 352.) 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
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STATS 261 |
Intermediate Biostatistics: Analysis of Discrete Data
[ syllabus ] |
K Sainani
Winter 2008-2009
(Same as BIOMEDIN 233, STATS 261.) Methods for analyzing data from case-control and cross-sectional studies: the 2x2 table, chi-square test, Fisher\'s exact test, odds ratios, Mantel-Haenzel methods, stratification, tests for matched data, logistic regression, conditional logistic regression. Emphasis is on data analysis in SAS. Special topics: cross-fold validation and bootstrap inference.
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STATS 305 |
STATS 305. Introduction to Statistical Modeling
[ syllabus ] |
Art Owen [ email ]
Autumn 2008-2009
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. Prerequisites: consent of instructor, 116, 200, applied statistics course, CS 106A, MATH 114.
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STATS 200 |
STATS 200. Introduction to Statistical Inference
[ syllabus ] |
Joseph Romano [ email ]
Winter 2008-2009
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.
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STATS 240 |
STATS 240. Statistical Methods in Finance
[ syllabus ] |
Tze Lai [ email ]
Autumn 2008-2009
(SCPD students register for 240P.) Regression analysis and applications to pricing and investment models. Principal components and multivariate analysis. Parametric influence. Financial time series. Estimation and modeling of volatilities. Statistical methods for portfolio management. Hands-on experience with financial data.
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STATS 203 |
STATS 203. Introduction to Regression Models and Analysis of Variance |
Nancy Zhang [ email ]
Winter 2008-2009
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.
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STATS 208 |
Introduction to the Bootstrap
[ syllabus ] |
Susan Holmes [ email ]
Spring 2008-2009
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.
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STATS 214 |
STATS 214. Randomness in the Physical World |
Susan Holmes [ email ]
Spring 2008-2009
(Same as APPPHYS 214.) 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.
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STATS 239a |
Workshop in Quantitative Finance |
Tze Lai [ email ]
Autumn 2008-2009
Topics of current interest.
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STATS 239b |
Workshop in Quantitative Finance |
Valdo Durrleman [ email ]
Winter 2008-2009
Topics of current interest.
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STATS 306A |
Methods for Applied Statistics |
Bradley Efron [ email ]
Winter 2008-2009
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.
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STATS 315A |
Modern Applied Statistics: Learning
[ syllabus ] |
Robert Tibshirani [ email ]
Autumn 2008-2009
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.
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STATS 252 |
STATS 252. Data Mining and Electronic Business |
Andreas Weigend [ email ]
Spring 2008-2009
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.
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STATS 262 |
Intermediate Biostatistics: Regression Prediction Survival Analysis
[ syllabus ] |
K Sainani
Spring 2008-2009
(Same as STATS 262.) 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.
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STATS 315B |
Modern Applied Statistics: Data Mining |
Jerome Friedman [ email ]
Winter 2008-2009
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.
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STATS 362 |
STATS 362. Monte Carlo Sampling |
Art Owen [ email ]
Autumn 2008-2009
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.
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## Anthropology | |||||

ANTHSCI 116 |
Data Analysis for Quantitative Research |
Ian Robertson [ email ]
Spring 2011-2012
This course allows graduate and advanced undergraduate students in archaeology and anthropology to acquire practical skills in quantitative data analysis. Some familiarity with basic statistical methods is useful but not assumed; the structure of the course will be flexible enough to accommodate a range of student expertise and interests. Topics covered include: statistics and graphics in R; database design, resampling methods, diversity measures, contingency table analysis, and introductory methods in spatial analysis.
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ANTHSCI 155 |
Research Methods in Ecological Anthropology |
Douglas Bird [ email ]
Spring 2011-2012
The course prepare students for the methodological and practical aspects of doing ecologically oriented, quantitative anthropological field research. The primary goal is to explore what it means to ask anthropological questions in a systematic way. We will focus on understanding what can constitute an interesting question, how to frame a question in way that facilitates investigation, and how to design methods to begin investigating a question. In turn, the course will provide a format to refine research projects in preparation for doing more extensive fieldwork.
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ANTHSCI 130D |
Spatial Approaches to Social Science (ANTHRO 230D) |
Claudia Engel [ email ]
Winter 2011-2012
This multidisciplinary course combines different approaches to how GIS and spatial tools can be applied in social science research. We take a collaborative, project oriented approach to bring together technical expertise and substantive applications from several social science disciplines. The course aims to integrate tools, methods, and current debates in social science research and will enable students to engage in critical spatial research and a multidisciplinary dialogue around geographic space.
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