Statistics

Faculty
Introduction
Statistics—B.S.
Course Descriptions

Faculty

Chairperson

Professor Stanley Wasserman

Rudy Professor of Statistics and Physics

Karen Kafadar

Rudy Professor of Statistics, Psychology, and Sociology

Stanley Wasserman

Chancellor's Professor of Sociology and Statistics

J. Scott Long

Professors

Steen Andersson, Lath Tran, Michael Trosset

Assistant Professor

Chunfeng Huang

Visiting Assistant Professor

Brian S. Marks

Adjunct Faculty

Franklin Acito (Kelley School of Business), Richard Bradley (Mathematics), Jerome Busemeyer (Psychological and Brain Sciences), Juan Carlos Escanciano (Economics), Victor Goodman (Mathematics), Andrew Hanson (School of Informatics), Elizabeth Housworth (Mathematics), David Jacho-Chavez (Economics), Kenneth Kelley (School of Education), John Kruschke (Psychological and Brain Sciences), Yoon Jin Lee (Economics), Russell Lyons (Mathematics), Robert Nosofsky (Psychological and Brain Sciences), Joanne Peng (School of Education), Christopher Raphael (School of Informatics), Scott Robeson (Geography), Richard Shiffrin (Psychological and Brain Sciences), Rusty Tchernis (Economics), James Townsend (Psychological and Brain Sciences), Pravin Trivedi (Economics), Konstantin Tyurin (Economics), Alessandro Vespigniani (School of Informatics)

Academic Advising

Statistics House 106, (812) 855-7828

Introduction

Statistics is the science of data. Data are numbers with a context; the particular context that gave rise to the numbers is important. In addition to a knowledge of mathematics, statisticians must learn about the scientific disciplines that generate data of interest to understand and explain the observational studies or the statistical experiments in question. For example, statisticians calculate probabilities for DNA paternity tests; design clinical trials to study the effectiveness of new medications; study economic time series data, such as gross domestic product from developing countries in Africa; and develop statistical models of responses from fMRI psychological experiments.

The field of statistics has a coherent body of theory, which students of the field master, as well as methodology designed for applied uses in many disciplines. The department teaches courses in both theoretical and applied statistics.

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Statistics—B.S.

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Course Descriptions

S100 Statistical Literacy (3 cr.) N & M P: MATH M014 or equivalent. How to be an informed consumer of statistical analysis. Experiments and observational studies, summarizing and displaying data, relationships between variables, quantifying uncertainty, drawing statistical inferences. S100 cannot be taken for credit if credit has already been received for any statistics course (in any department) numbered 300 or higher. Credit given for only one of S100 or H100.

H100 Statistical Literacy, Honors (3 cr.) N & M P: MATH M014 or equivalent and permission of the Hutton Honors College. How to be an informed consumer of statistical analysis. Experiments and observational studies, summarizing and displaying data, relationships between variables, quantifying uncertainty, drawing statistical inferences. H100 cannot be taken for credit if credit has already been received for any statistics course (in any department) numbered 300 or higher. Credit given for only one of H100 or S100.

S300 Introduction to Applied Statistical Methods (4 cr.) N & M P: MATH M014 or equivalent. Introduction to methods for analyzing quantitative data. Graphical and numerical descriptions of data, probability models of data, inference about populations from random samples. Regression and analysis of variance. Lecture and laboratory. Credit given for only one of the following: S300, CJUS K300, ECON E370 or S370, LAMP L316, MATH K300 or K310, PSY K300 or K310, SOC S371, SPEA K300.

S320 Introduction to Statistics (3 cr.) N & M P: MATH M212 or M301 or M303. Basic concepts of data analysis and statistical inference, applied to 1-sample and 2-sample location problems, the analysis of variance, and linear regression. Probability models and statistical methods applied to practical situations using actual data sets from various disciplines. Credit given for only one of S320 or MATH M365.

S420 Introduction to Statistical Theory (3 cr.) P: STAT S320 and MATH M463, or consent of instructor. Fundamental concepts and principles of data reduction and statistical inference, including the method of maximum likelihood, the method of least squares, and Bayesian inference. Theoretical justification of statistical procedures introduced in S320.

S425 Nonparametric Theory and Data Analysis (3 cr.) P: S420 and S432, or consent of instructor. Survey of methods for statistical inference that do not rely on parametric probability models. Statistical functionals, bootstrapping, empirical likelihood. Nonparametric density and curve estimation. Rank and permutation tests.

S426 Bayesian Theory and Data Analysis (3 cr.) P: S420 and S432 or consent of instructor. Introduction to the theory and practice of Bayesian inference. Prior and Posterior probability distributions. Data collection, model formulation, computation, model checking, sensitivity analysis.

S431 Applied Linear Models I (3 cr.) P: STAT S320 and MATH M301 or M303 or S303, or consent of instructor. Part I of a two-semester sequence on linear models, emphasizing linear regression and the analysis of variance, including topics from the design of experiments and culminating in the general linear model.

S432 Applied Linear Models II (3 cr.) P: S431, or consent of instructor. Part II of a two-semester sequence on linear models, emphasizing linear regression and the analysis of variance, including topics from the design of experiments and culminating in the general linear model.

S437 Categorical Data Analysis (3 cr.) P: S420 and S432 or consent of instructor. The analysis of cross-classified categorical data. Loglinear models; regression models in which the response variable is binary, ordinal, nominal, or discrete. Logit, probit, multinomial logit models; logistic and Poisson regression.

S439 Multilevel Models (3 cr.) P: S420 and S432 or consent of instructor. Introduction to the general multilevel model with an emphasis on applications. Discussion of hierarchical linear models and generalizations to nonlinear models. How such models are conceptualized, parameters estimated and interpreted. Model fit via software. Major emphasis throughout the course will be on how to choose an appropriate model and computational techniques.

S440 Multivariate Data Analysis (3 cr.) P: S420 and S432 or consent of instructor. Elementary treatment of multivariate normal distributions, classical inferential techniques for multivariate normal data, including Hotelling's T² and MANOVA. Discussion of analytic techniques such as principal component analysis, canonical correlation analysis, discriminant analysis, and factor analysis.

S445 Covariance Structure Analysis (3 cr.) P: S420 and S440, or consent of instructor. Path analysis. Introduction to multivariate multiple regression, confirmatory factor analysis, and latent variables. Structural equation models with and without latent variables. Mean-structure and multi-group analysis.

S450 Time Series Analysis (3 cr.) P: MATH M466 or STAT S420, and STAT S432, or consent of instructor. Techniques for analyzing data collected at different points in time. Probability models, forecasting methods, analysis in both time and frequency domains, linear systems, state-space models, intervention analysis, transfer function models and the Kalman filter. Topics also include: stationary processes, autocorrelations, partial autocorrelations, autoregressive, moving average, and ARMA processes, spectral density of stationary processes, periodograms and estimation of spectral density.

S455 Longitudinal Data Analysis (3 cr.) P: S420 and S432 or consent of instructor. Introduction to methods for longitudinal data analysis; repeated measures data. The analysis of change-models for one or more response variables, possibly censored. Association of measurements across time for both continuous and discrete responses.

S460 Sampling (3 cr.) P: S420 and S432, or consent of instructor. Design of surveys and analysis of sample survey data. Simple random sampling, ratio and regression estimation, stratified and cluster sampling, complex surveys, nonresponse bias.

S470 Exploratory Data Analysis (3 cr.) P: S420 and S432 or consent of instructor. Techniques for summarizing and displaying data. Exploration versus confirmation. Connections with conventional statistical analysis and data mining. Application to large data sets.

S475 Statistical Learning and High-Dimensional Data Analysis (3 cr.)
P: S440 or consent of instructor. Data-analytic methods for exploring the structure of high-dimensional data. Graphical methods, linear and nonlinear dimension reduction techniques, manifold learning. Supervised, semi-supervised, and unsupervised learning.

S481 Topics in Applied Statistics (3 cr.) P: Consent of instructor. Careful study of a statistical topic from an applied perspective. May be repeated with different topics for a maximum of 12 credit hours.

S482 Topics in Mathematical Statistics (3 cr.) P: Consent of instructor. Careful study of a statistical topic from a theoretical perspective. May be repeated with different topics for a maximum of 12 credit hours.

S490 Statistical Consulting (4 cr.) P: Consent of instructor. Development of effective consulting skills, including the conduct of consulting sessions, collaborative problem-solving, using professional resources, and preparing verbal and written reports. Interactions with clients will be coordinated by the Indiana Statistical Consulting Center.

S495 Readings in Statistics (1-3 cr.) P: Consent of instructor. Supervised reading of a topic in statistics. May be repeated with different topics for a maximum of 12 credit hours.

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