Programs by Campus

Bloomington

Statistics
Courses

Curriculum
Courses
Faculty

  • STAT–S 501 Statistical Methods I: Introduction to Statistics (3 cr.) P: One undergraduate course in statistics. This course takes a systematic approach to the exposition of the general linear model — focusing on correlation, simple linear and multiple regression. Students are introduced to the use of statistical analysis software. The first third of the course consists of a review of statistics, data analysis tools, significance tests, and confidence intervals. Students learn how to think creatively about the use of statistical methods in their own research.
  • STAT–S 503 Statistical Methods II: Generalized Linear Models and Categorical Data (3 cr.) P: STAT S501 or one undergraduate course in statistics. This course takes a systematic approach to the exposition of the general linear model — focusing on categorical data. Of primary concern will be models for which the response variable is categorical. Such models include probit, logit, ordered logit, and Poisson regression, among others. Students learn how to think creatively about the use of statistical methods in their own research.
  • STAT–S 520 Introduction to Statistics (3 cr.) P: MATH M212, M301, M303, or the equivalent. 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 and actual data sets from various disciplines. Elementary statistical theory, including the plug-in principle, maximum likelihood, and the method of least squares.
  • STAT–S 620 Introduction to Statistical Theory (3 cr.) P: STAT S320 and MATH M463 (or equivalent courses). 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.
  • STAT–S 625 Nonparametric Theory and Data Analysis (3 cr.) P: Two statistics courses at the graduate level, 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.
  • STAT–S 626 Bayesian Theory and Data Analysis (3 cr.) P: Two statistics courses at the graduate level, 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.
  • STAT–S 631 Applied Linear Models I (3 cr.) P: STAT S320 and MATH M301 or M303 or S303 (or equivalent courses), or consent of instructor. Part I of a 2-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.
  • STAT–S 632 Applied Linear Models II (3 cr.) P: STAT S631, or consent of instructor. Part II of a 2-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.
  • STAT–S 637 Categorical Data Analysis (3 cr.) P: Two statistics courses at the graduate level, 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. Equivalent to EDUC Y637.
  • STAT–S 639 Multilevel Models (3 cr.) P: Two statistics courses at the graduate level, 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. Equivalent to EDUC Y639.
  • STAT–S 640 Multivariate Data Analysis (3 cr.) P: Two statistics courses at the graduate level, 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. Equivalent to PSY P654.
  • STAT–S 645 Covariance Structure Analysis (3 cr.) P: Two statistics courses at the graduate level, 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. Equivalent to EDUC Y645.
  • STAT–S 650 Time Series Analysis (3 cr.) P: Two statistics courses at the graduate level, 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. Stationary processes, autocorrelations, partial autocorrelations, autoregressive, moving average, and ARMA processes, spectral density of stationary processes, periodograms, estimation of spectral density. Course equivalent to MATH M568.
  • STAT–S 655 Longitudinal Data Analysis (3 cr.) P: Two statistics courses at the graduate level, 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. Course is equivalent to EDUC Y655.
  • STAT–S 660 Sampling (3 cr.) P: Two statistics courses at the graduate level, 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.
  • STAT–S 670 Exploratory Data Analysis (3 cr.) P: Two statistics courses at the graduate level, or consent of instructor. Numerical and graphical techniques for summarizing and displaying data. Ex­ploration versus confirmation. Connections with conventional statistical analysis and data mining. Applications to large data sets.
  • STAT–S 670 Statistical Learning and High-Dimension Analysis (3 cr.) P: STAT S640, or two statistics courses at the graduate level, or consent of instructor. Dataanalytic methods for exploring the structure of high-dimensional data. Graphical methods, linear and nonlinear dimension reduction techniques, manifold learn­ing. Supervised, semisupervised, and unsupervised learning.
  • STAT–S 681 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.
  • STAT–S 682 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.
  • STAT–S 690 Topics in Mathematical Statistics (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.
  • STAT–S 695 Readings in Statistics (1–3 cr.) P: Consent of instructor. Supervised reading of a topic in statistics. May be repeated with different topics.
  • STAT–S 710 Statistical Computing (3 cr.) P: STAT S620, or consent of instructor. Survey of numerical methods in statistics. Matrix factorizations and algorithms for linear regression. Nonlinear optimization, maximum likelihood and nonlinear regression. Pseudorandom number generation and Monte Carlo methods.
  • STAT–S 721 Advanced Statistical Theory I (3 cr.) P: S620, some knowledge of elementary measure theory, and/or consent of the instructor. Mathematical introduction to major areas of statistical theory and practice, including statistical models, suf­ficiency, likelihood inference, estimation and testing, Bayesian inference, decision theory, equivariance, and optimality of test statistics.
  • STAT–S 722 Advanced Statistical Theory II (3 cr.) P: S721 or consent of the instructor. A continuation of S721. A mathematical intro­duction to major areas of statistical theory and practice includ­ing multinomial models, canonical linear models, exponential families, asymptotic theory, and general linear models.
  • STAT–S 730 Theory of Linear Models (3 cr.) P: STAT S620, or consent of instructor. Theory of the general linear model. Distribution theory, linear hypotheses, the Gauss-Markov theorem, test­ing and confidence regions. Application to regression and to analysis of variance.
  • STAT–S 740 Multivariate Statistical Theory (3 cr.) P: STAT S721 and S722, or consent of the instructor. Multivariate normal distri­butions. Multivariate linear normal models, estimation and testing. Wishart distributions and models. Inference for the covariance matrix. Eigenvalues, including canonical correlations and principal components/factor analysis.
  • STAT–S 781 Advanced Topics in Applied Statistics (3 cr.) P: Consent of the instructor. Careful study of an advanced statistical topic from an applied perspective. As topics vary, this course may be repeated for credit.
  • STAT–S 782 Advanced Topics in Mathematical Statistics (3 cr.) P: Consent of the instructor. Careful study of an advanced statistical topic from a mathematical or theoretical perspective. As topics vary, this course may be repeated for credit.
  • STAT–S 799 Research in Statistics (1–6 cr.) P: Consent of the instructor. Research in statistics.

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