STAT-S 100 Statistical Literacy (3 cr.)

**CASE N&M**P: MATH-M 014 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. Credit given for only one of S100 or H100.

STAT-H 100 Statistical Literacy, Honors (3 cr.)

**CASE N&M**P: MATH-M 014 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. Credit given for only one of H100 or S100.

STAT-S 201 Networks 2.0: Quantitative Literacy (3 cr.)

**CASE N&M**P: STAT-S 100 or any other introductory statistics course or permission of instructor. How to understand, analyze, and view networks. Topics include network visualization, data gathering, and an overview of network theory and analysis. Students learn basic network terminology and see examples of network methodology, studying a wide variety of network structural analyses designed to illustrate network theories. Possible applications to social and behavioral sciences, information science, political science, public health, and Facebook.

STAT-S 211 Statistics for Journalists (3 cr.)

**CASE N&M**P: MATH-M 014 or equivalent. Essential statistical concepts and tools for journalists in the age of data, including probability, graphics, descriptive statistics, prediction, study design, comparison, testing, and estimation. The course has a heavier emphasis on writing and reading media reports than other introductory statistics courses.

STAT-S 300 Introduction to Applied Statistical Methods (4 cr.)

**CASE N&M**P: MATH-M 014 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 STAT S300 or K310 or S301, 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.)

**CASE N&M**P: MATH-M 118 or equivalent. 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-S 303 Applied Statistical Methods for the Life Sciences (3 cr.)

**CASE N&M**P: MATH-M 014. Introduction to methods for analyzing data arising in the life sciences, designed for biology, human biology, and pre-medical students. Graphical and numerical descriptions of data, probability models, fundamental principles of estimation and hypothesis testing, inferences about means, correlation, linear regression. Credit given for only one of the following: STAT- S 300, S301, S303 or K310; ANTH-A 306; CJUS-K 300; ECON-E 370 or S370; MATH-K 300 or K310; POLS-Y 395; PSY-K 300 or K310; SOC-S 371; or SPEA-K 300.

STAT-K 310 Statistical Techniques (3 cr.)

**CASE N&M**P: MATH-M 119 or equivalent. 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 or S301, 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. 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 STAT-S 320 or STAT-S 350.

STAT-S 350 Introduction to Statistical Inference (3 cr.) P: MATH-M 118 and M 119 and a previous statistics course; MATH-M 119 and MATH-X 201; or MATH-M 211; or MATH-M 212; or permission of the instructor. Formulation of statistical inference using probability models. Point estimation, hypothesis testing, and set estimation for various models, including 1-, 2-, and K-sample location problems, goodness-of-fit, correlation and regression. Credit given for only one of STAT-S 350 or STAT-S 320.

STAT-S 352 Data Modeling and Inference (3 cr.) P: STAT-S 320 or STAT-S 350; or consent of instructor. Intermediate-level survey of resampling, likelihood, and Bayesian methods of statistical inference. Distributional models of various data types. Categorical, count, time-to-event, time series, linear models, and hierarchical regression models.

STAT-S 420 Introduction to Statistical Theory (3 cr.) P: STAT-S 320 and MATH-M 463, 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: STAT-S 420 and STAT-S 432, 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: STAT-S 420 and STAT-S 432; 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: One of STAT-S 320 or STAT-S 350; and one of MATH-M 301, MATH-M 303, MATH-S 303, or STAT-S 352; 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: STAT-S 431, 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: STAT-S 420 and STAT-S 432 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: STAT-S 420 and STAT-S 432 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: STAT-S 420 and STAT-S 432, or consent of instructor. Elementary treatment of multivariate normal distributions, classical inferential techniques for multivariate normal data, including Hotelling’s T

^{2}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: STAT-S 420 and STAT-S 440, 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-M 466 or STAT-S 420, and STAT-S 432, 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: STAT-S 420 and STAT-S 432 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: STAT-S 420 and STAT-S 432, 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: STAT-S 352 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: STAT-S 440 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-X 473 Internship in Statistical Consulting (1-3 cr.) P: STAT-S 490 or STAT-X 498; and consent of the Director of Undergraduate Studies. One-semester internship at the Indiana Statistical Consulting Center (ISCC). Students work on actual consulting problems under the direct supervision of professional statisticians. S/F grading. Credit given for only one of STAT-X 473 or STAT-S 492.

STAT-X 490 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 in STAT-X 490 and STAT-S 495.

STAT-X 498 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. Credit given for only one of STAT-X 498 or STAT-S 490.