Full Course List

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 202 and 210.

210-0 Introductory Statistics for the Social Sciences

Introduction to basic concepts and methods of statistics and probability. Methods of data collection, descriptive statistics, probability, estimation, sampling distributions, confidence intervals, hypothesis testing. May not receive credit for both 202 and 210. 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 to be covered include simple linear regression, multiple regression, analysis of variance, and analysis of covariance. Practical application of the methodology and the interpretation of the results will be emphasized. Examples will be drawn from a wide range of fields. Prerequisites: 202, 210, or equivalent; MATH 220.

301-1,2,3 Data Science

This series aims to have students learn the practical skills necessary for conducting data science while concurrently surveying foundational analytic methods with a focus on application.

301-1: Focus of this course will be the development of basic practical skills necessary for conducting data science, such as data management, manipulation, and visualization. Students will develop best practices to efficiently carryout these skills. Methods addressed include linear regression and classification, model selection, and model assessment. Prerequisite: 210 or equivalent.

301-2: Focus of this course will be the development of skills, tools, and strategies that will enable students to effectively communicate data and analyses to diverse audiences. Methods addressed include generalized additive models, regression and classification trees, basis expansions, and smoothing. Prerequisite: 301-1 or consent of instructor.

301-3: Focus of this course will be the development of advanced data analytic methods and tools. Methods addressed include projection pursuit, neural networks, vector support machines, Bayesian model averaging and stacking, and clustering. Substantial data analysis project required. Prerequisite: 301-2 or consent of instructor.

320-1,2,3 Statistical Theory and Methods


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

320-2: Sampling, parameter estimation, confidence intervals, hypothesis tests. Prerequisite: 320-1.

320-3: Comparison of parameters, goodness-of-fit tests, regression analysis, analysis of variance, and nonparametric methods. Prerequisites: 320-2, MATH 240.

325-0 Survey Sampling

Probability sampling, simple random sampling, error estimation, determination of sample size, stratification, systematic sampling, replication and pseudo- replication methods, ratio and regression estimation, cluster sampling, multiphase sampling, and nonsampling errors. Prerequisites: MATH 230; either 1 300-level course in Statistics other than STAT 330 or 1 of ECON 381-1,2, IEMS 303, 304, MATH 385, 386-1,2; or permission of instructor.

328-0 Causal Inference

This course is an introduction to modern statistical thinking about causal inference.  We will consider the completely randomized experiment as the basic design for causal inference, looking at this framework from the randomization, sampling, and Bayesian perspectives.  We will examine methods for matching, including methods based on the linear discriminant score, propensity scores, the Mahalonobis metric, and combinations of these.  Applications of these methods to observational studies and studies with noncompliance will also be discussed. Prerequisites: 320-2, 350.

338-0 History of Statistics

Historical survey of the development of modern statistics, from Bernoulli’s law of large numbers to the contributions of R.A. Fisher. Prerequisite: 320-2 or equivalent.

342-0 Statistical Data Mining

The goal of this course is to present statistical methods for describing the relationships between a binary response variable and multiple explanatory variables. Topics covered will include decision theory, logistic regression, neural networks, decision trees, clustering, and association rules. Prerequisites: courses in probability and statistics comparable to 320-1,2; a course in multiple regression comparable to 350; 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 programing. Prerequisites: 2 courses chosen from 320-2,3, 350, 351, PSYCH 351, MATH 240, or equivalent.

345-0 Statistical Demography

Self contained introduction to statistical theory of demographic rates (births, deaths, migration) in multi-state setting; statistical model underlying formal demography; analysis of error in demographic forecasting. Prerequisite: 350, MATH 240, or equivalent.

348-0 Applied Multivariate Analysis

The goal of this course is to present statistical methods for describing and analyzing multivariate data, data in which the response is multidimensional. The topics covered include principal component analysis, canonical correlation, multidimensional scaling,factor analysis, and clustering. Although the theory behind the methods will be discussed, the course will emphasize the statistical and geometric motivation for the methods, the practical application of the methods, and the interpretation of the results. Prerequisites: 320-2, MATH 240.

350-0 Regression Analysis

Development of statistical techniques for linear regression, with an emphasis on applications to empirical data. Least-squares methods, confidence intervals, tests of hypotheses, measurement of association, and residual analysis. Criteria and methods of model selection. Computational and inferential procedures for nonlinear regression. Use of computer packages is emphasized throughout the course. Co-requisite: 320-2 or equivalent.

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, Latin squares, Youden squares, factorial and fractional factorial designs, random-effects and mixed-effects models, and split-plot and nested designs. Prerequisite: 320-2 or equivalent.

352-0 Nonparametric Statistical Methods

The goal of this course is to present an introduction to nonparametric function estimation. The topics covered include estimation of a distribution function, nonparametric density estimation, nonparametric regression, and semiparametric regression. The emphasis will be on understanding the basic theory of these methods and using the methods in data analysis. Prerequisite: 320-2 or equivalent.

 354-0 Applied Time Series Modeling and Forecasting

The goal of this course is to provide an introduction to the theory and methods of modern time series analysis. The main focus of the course is on the modeling and forecasting of time series using linear models. Other topics to be discussed include spectral densities, periodograms, regression models with correlated errors, smoothing methods, and GARCH models. Prerequisite: 320-1. Corequisite: 350

355-0 Analysis of Qualitative Data

An introduction to the analysis of qualitative data with emphasis on the use of log-linear models. Topics include polytomous responses, two-way tables, multiway tables, logits, multinomial responses, incomplete tables, symmetric tables, adjustment techniques, and latent-class models. Prerequisite: 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: 320-2, 350.

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

The goal of this course is to present an introduction to the use of statistical methods in analyzing financial data. The topics covered include models for returns, the random walk hypothesis, portfolio theory, the capital asset pricing model, and regression models for return data. The emphasis will be on understanding the basic concepts of finance and using those to guide the statistical analysis of financial data. Prerequisites: MATH 240 and at least 2 courses in probability and statistics.

370-0 Human Rights Statistics

We consider uses of statistics — as numbers and as methodology — for use in the context of Human Rights (HR). We take as HR those stipulated in existing international laws. The emphasis of the course is on the discovery and critique of statistical methods used in the HR context, including development, analysis, interpretation, use, and misuse of statistical data for description, assessment, evaluation, and political action. Examples of HR topics include missing females, deaths of combatants and noncombatants from war, disappearances, criminal justice, violence against women, trafficking, child labor, profiling, free elections, hunger, refugees, poverty, illegal immigration, hate crimes and discrimination. Prerequisites: 2 of 325, 350, 320-2,3; ECON 381-1,2; MATH 386-1,2; IEMS 303, 304; or consent of instructor.

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 383 and any of 320-1; MATH 310-1, 314, 385; EECS 302; or IEMS 202. Prerequisites: MATH 281-1,2,3; PHYSICS 125-1,2,3.

398 Undergraduate Seminar

399 Independent Study

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