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Course Descriptions

Undergraduate Courses

Courses Open to Undergraduate and Graduate Students

Graduate Courses

Undergraduate Courses

202-0 – Introduction to Statistics

Data collection, summarization, correlation, regression, probability, sampling, estimation, tests of significance. Does not require calculus and makes minimal use of mathematics. May not receive credit for both STAT 202-0 and STAT 210-0.

210-0 – Introductory Statistics for the Social Sciences

A mathematical introduction to probability theory and statistical methods, including properties of probability distributions, sampling distributions, estimation, confidence intervals, and hypothesis testing. STAT 210-0 is primarily intended for economics majors. May not receive credit for both STAT 202-0 and STAT 210-0. Prerequisite: strong background in high school algebra (calculus is not required).

232-0 – Applied Statistics

Basic concepts of using statistical models to draw conclusions from experimental and survey data. Topics include simple linear regression, multiple regression, analysis of variance, and analysis of covariance. Practical application of the methods and the interpretation of the results will be emphasized. Prerequisites: STAT 202-0, STAT 210-0, or equivalent; MATH 220-0.

320-3 – Statistical Theory and Methods 3

Comparison of parameters, goodness-of-fit tests, regression analysis, analysis of variance, and nonparametric methods. Prerequisites: STAT 320-2, MATH 240-0.

332-0 – Statistics for Life Sciences

Application of statistical methods and data analysis techniques to the life sciences. Parametric statistics, nonparametric approaches, resampling-based approaches. Prerequisite: 1 introductory statistics course.

*NOTE: This course does not count toward a statistics major or minor

354-0 – Applied Time Series Modeling and Forecasting

Introduction to modern time series analysis. Autocorrelation, time series regression and forecasting, ARIMA and GARCH models. Prerequisites: STAT 320-1. Corequisite: STAT 350-0.

370-0 – Human Rights Statistics

Development, analysis, interpretation, use, and misuse of statistical data and methods for description, evaluation, and political action regarding war, disappearances, justice, violence against women, trafficking, profiling, elections, hunger, refugees, discrimination, etc. Prerequisites: Two of STAT 325-0, STAT 350-0, STAT 320-2,STAT 302-3; or ECON 381-1, ECON 381-2; or MMSS 386-1, MMSS 386-2; or IEMS 303-0, IEMS 304-0.

383-0 – Probability and Statistics for ISP

Probability and statistics. Ordinarily taken only by students in ISP; permission required otherwise. May not receive credit for both STAT 383-0 and any of STAT 320-1; MATH 310-1, MATH 311-1, MATH 314-0, MATH 385-0; EECS 302-0; or IEMS 202-0. Prerequisites: MATH 281-1,MATH 281-2, MATH 281-3; PHYSICS 125-1, PHYSICS 125-2, PHYSICS 125-3.

*NOTE: This course does not count toward a statistics major or minor

398 – Undergraduate Seminar

No description available.

399 – Independent Study

Independent work under the guidance of a faculty member. Consent of department required.

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Courses Open to Undergraduate and Graduate Students

301-1 – Data Science 1

Data Science 1 focuses on data management, manipulation, and visualization skills and techniques for exploratory data analysis. Prerequisite: STAT 202-0 or equivalent

301-2 – Data Science 2

Data Science 2 focuses on foundational analytic methods such as linear regression, resampling, and tree-based methods. Prerequisite: STAT 301-1 or consent of instructor.

301-3 – Data Science 3

Data Science 3 focuses on methods such as support vector machines, clustering, and neural networks. Prerequisite: STAT 301-2 or consent of instructor.

302 – Data Visualization

Introduction to the knowledge, skills, and tools required to visualize data of various formats across statistical domains and to create quality visualizations for both data exploration and presentation. Prerequisite: STAT 202-0 or equivalent.

320-1 – Statistical Theory and Methods 1

Sample spaces, computing probabilities, random variables, distribution functions, expected values, variance, correlation, limit theory. May not receive credit for both STAT 320-1 and any of STAT 383-0, MATH 310-1, MATH 311-1, MATH 314-0, MATH 385-0, EECS 302-0, or IEMS 202-0. Corequisites: STAT 202-0 or STAT 210-0, MATH 234-0.

320-2 – Statistical Theory and Methods 2

Sampling, parameter estimation, confidence intervals, hypothesis tests. Prerequisite: STAT 320-1 or MATH 310-1.

325-0 – Survey Sampling

Probability sampling, simple random sampling, error estimation, sample size, stratification, systematic sampling, replication methods, ratio and regression estimation, cluster sampling. Prerequisites: MATH 230 and 2 quarters of statistics, or consent of instructor.

328-0 – Causal Inference

Introduction to modern statistical thinking about causal inference. Topics include completely randomized experiments, confounding, ignorability of assignment mechanisms, matching, observational studies, noncompliance, and Bayesian methods. Prerequisites: STAT 320-2, STAT 350-0.

342-0 – Statistical Data Mining

Methods for modeling binary responses with multiple explanatory variables. Potential topics include statistical decision theory, binary regression models, cluster analysis, probabilistic conditional independence, and graphical models. Prerequisites: courses in probability and statistics comparable to STAT 320-1, STAT 320-2; a course in multiple regression comparable to STAT 350-0; familiarity with statistical computing software such as MINITAB or SPSS.

344-0 – Statistical Computing

Exploration of theory and practice of computational statistics with emphasis on statistical programming in R. Prerequisite: STAT 320-2 or equivalent.

345-0 – Statistical Demography

Introduction to statistical theory of demographic rates (births, deaths, migration) in multistate setting; statistical models underlying formal demography; analysis of error in demographic forecasting. Prerequisite: STAT 350-0, MATH 240-0, or equivalent.

348-0 – Applied Multivariate Analysis

Statistical methods for describing and analyzing multivariate data. Principal component analysis, factor analysis, canonical correlation, clustering. Emphasis on statistical and geometric motivation, practical application, and interpretation of results. Prerequisites: STAT 320-2, MATH 240-0.

350-0 – Regression Analysis

Simple linear regression and correlation, multiple regression, residual analysis, selection of subsets of variables, multi-collinearity and shrinkage estimation, nonlinear regression. Prerequisite or corequisite: STAT 320-2

351-0 – Design and Analysis of Experiments

Methods of designing experiments and analyzing data obtained from them: one-way and two-way layouts, incomplete block designs, factorial designs, random effects, split-plot and nested designs. Prerequisite: STAT 320-1 or equivalent.

352-0 – Nonparametric Statistical Methods

Survey of nonparametric methods, with emphasis on understanding their application. Estimation of a distribution function, density estimation, and nonparametric regression. Prerequisite: STAT 350-0

355-0 – Analysis of Qualitative Data

Introduction to the analysis of qualitative data. Measures of association, loglinear models, logits, and probits. Prerequisite: STAT 320-2 or equivalent.

356-0 – Hierarchical Linear Models

Introduction to the theory and application of hierarchical linear models. Two and three level linear models, hierarchical generalized linear models, and application of hierarchical models to organizational research and growth models. Prerequisites: STAT 320-2, STAT 350-0.

359-0 – Topics in Statistics

Topics in theoretical and applied statistics, to be chosen by the instructor. This course may be taken more than once for credit. Prerequisite: consent of instructor.

365-0 – Introduction to Financial Statistics

Statistical methods for analyzing financial data. Models for asset returns, portfolio theory, parameter estimation. Prerequisites: STAT 320-3, MATH 240-0.

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Graduate Courses

330-1 – Applied Statistics for Research 1

First Quarter: Design of experiments and surveys, numerical summaries of data, graphical summaries of data, correlation and regression, probability, sample mean, sample proportion, confidence intervals and tests of significance, one and two sample problems, ANOVA. Second Quarter: Simple linear regression, inference, diagnostics, multiple regression diagnostics, autocorrelation, 1-way ANOVA, power and sample size determination, 2-way ANOVA, ANCOVA, randomized block designs.

330-2 – Applied Statistics for Research 2

Second Quarter: Simple linear regression, inference, diagnostics, multiple regression diagnostics, autocorrelation, 1-way ANOVA, power and sample size determination, 2-way ANOVA, ANCOVA, randomized block designs.

420-1 – Intr to Statistical Theory & Methodology 1

Distribution theory, characteristic functions, moments and cumulants, random variables, sampling theory, and common statistical distributions.

420-2 – Stat Theory/Meth 2

Methods of estimation, hypothesis tests, confidence intervals, least squares, likelihood methods, and large-sample methods.

420-3 – Intr to Statistical Theory & Methodology 3

Theories of inference, multivariate methods, and contingency tables.

439-0 – Meta-Analysis

Statistical methods for combining results of replicated experiments. Effect size indexes and their estimators, combined estimation and test of heterogeneity, modeling between-study variation in effect sizes, models for publication selection. Prerequisites: A graduate-level course in statistics.

448-0 – Multivariate Statistical Methods

Multivariate normal distribution, Hotelling's T2-test, multivariate analysis of variance, discriminant analysis, canonical correlation, principal components, and factor analysis. Use of computer packages.

451-0 – Design & Analysis of Social Experiments

This course covers the design and analysis of social experiments conducted in field settings. It will focus on experiments based on samples from populations with hierarchical structure and experiments that involve randomization of intact groups (statistical clusters) to treatments. Design and analysis considerations will be covered in detail, and students will carry out exercises in the design and analysis of social experiments in realistic settings. Prerequisites: Permission of the instructor.

453-0 – Survival Analysis

Life-table construction, Kaplan-Meier estimation, exponential survival distributions, Weibull distributions, and Cox regression models.

454-0 – Time Series Analysis

Harmonic analysis, power spectra, filtering, cross-spectra, linear processes, and forecasting.

455-0 – Advanced Qualitative Data Analysis

Probit, logit, log-linear, and latent-class models. Multi-dimensional contingency tables; polytomous responses with continuous independent variables.

456-0 – Generalized Linear Models

Inference and fitting of generalized linear models with application to classical linear models, binomial and multinomial logit models, log-linear models, Cox's proportional hazards model and GEE's for longitudinal data. Prerequisites: STAT 350-0 and STAT 420-3

457-0 – Applied Bayesian Inference

Introduction to computational algorithms for Bayesian inference. Observed data and data augmentation methods are considered in detail. Methods are illustrated with real examples. Prerequisites: STAT 350-0 and STAT 420-1 or equivalent.

461-0 – Advanced Topics in Statistics

No description available.

465-0 – Statistical Methods for Bioinformatics and Computational Biology

An introduction of statistical methodologies in cutting-edge fields of computational biology and bioinformatics topics including microarray gene expression data analysis; biological sequence analysis; EST and SAGE data analysis.

466-0 – Likelihood Methods

Recent results in the theory of likelihood-based inference. Topics covered will include higher-order asymptotic theory, based both on Edgeworth expansions and saddlepoint methods, conditional and marginal likelihood functions, the modified profile likelihood function and adjustments to the signed likelihood ratio statistic. Prerequisites: STAT 420-2.

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