Departments & Programs

Statistics

Course Descriptions

  • STAT–S 100 Statistical Literacy (3 cr.) P: MATH M014 or equivalent. N & M 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.
  • STAT–H 100 Statistical Literacy, Honors (3 cr.) P: MATH M014 or equivalent and permission of the Hutton Honors College. N & M 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.
  • STAT–S 300 Introduction to Applied Statistical Methods (3 cr.) P: MATH M014 or equivalent. N & M  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 S300 or K310, ANTH A306, CJUS K300, ECON E370 or S370, MATH K300 or K310, POLS Y395, PSY K300 or K310, SOC S371, or SPEA K300.
  • STAT–S 301 Applied Statistical Methods for Business (3 cr.) P: Math M118 or equivalent. N & M  Introduction to methods for analyzing data arising in business, designed to prepare business students for the Kelley School’s Integrative Core. Graphical and numerical descriptions of data, probability models, fundamental principles of estimation and hypothesis testing, applications to linear regression and quality control. Microsoft Excel used to perform analyses. Credit given for only one of S301, K310 or S300, ANTH A306, CJUS K300, ECON E370 or S370, POLS Y395, MATH K300 or K310, PSY K300 or K310, SOC S371, or SPEA K300.
  • STAT–K 310 Statistical Techniques (3 cr.) P: MATH M119 or equivalent. N & M  Introduction to probability and statistics. Elementary probability theory, conditional probability, independence, random variables, discrete and continuous probability distributions, measures of central tendency and dispersion. Concepts of statistical inference and decision: estimation, hypothesis testing, Bayesian inference, statistical decision theory. Special topics discussed may include regression and correlation, time series, analysis of variance, nonparametric methods. Credit given for only one of K310 or S300, ANTH A306, CJUS K300, ECON E370 or S370, MATH K300 or K310, POLS Y395, PSY K300 or K310, SOC S371, or SPEA K300.
  • STAT–S 320 Introduction to Statistics (3 cr.) P: MATH M212 or M301 or M303. N & M  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.
  • STAT–S 420 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.
  • STAT–S 425 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.
  • STAT–S 426 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.
  • STAT–S 431 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.
  • STAT–S 432 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.
  • STAT–S 437 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.
  • STAT–S 439 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.
  • STAT–S 440 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.
  • STAT–S 445 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.
  • STAT–S 450 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.
  • STAT–S 455 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.
  • STAT–S 460 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.
  • STAT–S 470 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.
  • STAT–S 475 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.
  • STAT–S 481 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.
  • STAT–S 482 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.
  • STAT–S 490 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.
  • STAT–S 495 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.