IU Indianapolis Campus Bulletin 2024-2025

Undergraduate

• STAT-I 190 Topics in Statistics for Undergraduates (1-5 cr.) Supervised reading course or special topics course at the freshman level. Prerequisites and course material vary with the topic.

• STAT-I 290 Topics in Statistics for Undergraduates (3 cr.) Supervised reading course or special topics course at the sophomore level. Prerequisites and course material vary with the topic.

• STAT-I 301 Elementary Statistical Methods I (3 cr.) P: MATH-I 110 or MATH-I 111, with a grade of C or better, or MATH-M 118 with a grade of C- or better, taken within last 3 terms or an appropriate ALEKS placement score. Not open to students in the Department of Mathematical Sciences. Introduction to statistical methods with applications to diverse fields. Emphasis on understanding and interpreting standard techniques. Data analysis for one and several variables, design of samples and experiments, basic probability, sampling distributions, confidence intervals and significance tests for means and proportions, and correlation and regression. Software is used throughout.

• STAT-I 350 Introduction to Statistics (3 cr.) P: MATH-I 166. A data-oriented introduction to the fundamental concepts and methods of applied statistics. The course is intended primarily for majors in the mathematical sciences (mathematics, actuarial sciences, mathematics education). The objective is to acquaint the students with the essential ideas and methods of statistical analysis for data in simple settings. It covers material similar to that of STAT-I 511, but with emphasis on more data-analytic material. Includes a weekly computing laboratory using Minitab.

• STAT-I 371 Prep for Actuarial Exam I (2 cr.) This course is intended to help actuarial students prepare for the SOA/CAS Exam P/1.

• STAT-I 390 Topics in Statistics for Undergraduates (3 cr.) Supervised reading course or special topics course at the junior level. Prerequisites and course material vary with the topic.

• STAT-I 416 Probability (3 cr.) P: MATH-I 261. An introduction to mathematical probability suitable as preparation for actuarial science, statistical theory, and mathematical modeling. General probability rules, conditional probability, Bayes theorem, discrete and continuous random variables, moments and moment generating functions, continuous distributions and their properties, law of large numbers, and central limit theorem.

• STAT-I 417 Statistical Theory (3 cr.) P: STAT-I 416. An introduction to the mathematical theory of statistical inference, emphasizing inference for standard parametric families of distributions. Properties of estimators. Bayes and maximum likelihood estimation. Sufficient statistics. Properties of test of hypotheses. Most powerful and likelihood-ratio tests. Distribution theory for common statistics based on normal distributions.

• STAT-I 421 Modern Statistical Modeling Using R and SAS (3 cr.) P: STAT-I 417 or equivalent. An introductory course on statistical computation. The primary goals of this course are (i) to introduce popular statistical software SAS and R and to develop basic data analysis skills, and (ii) to introduce basic statistical computation methods used in applications.

• STAT-I 432 Introduction to Stochastic Process and Probability Modeling (3 cr.) P: STAT-I 416 or equivalent. The course builds on elementary probability theory and introduces stochastic processes applied to the study of phenomena in fields such as engineering, computer science, management science, the life, physical and social sciences, and operations research. The approach is heuristic and non-rigorous. It develops students’ intuitive feel for the subject and enables them to think probabilistically. Computation is emphasized and requires use of software such as Excel, MINITAB, and R.

• STAT-I 433 Introduction to Nonparametric Statistics (3 cr.) P: STAT-I 417 and STAT-I 421 or equivalents. The course acquaints students with rank-based, permutation-based and resampling-based methods of statistical analysis used in widely applicable settings where the data do not follow parametric models. It extends techniques taught in STAT-I 350 / STAT-I 511, where the normal theory is assumed, to situations where the normal theory does not hold. It includes computer projects which use statistical software such as R and SAS.

• STAT-I 472 Actuarial Models I (3 cr.) P: STAT-I 417 or equivalent. Mathematical foundations of actuarial science emphasizing probability models for life contingencies as the basis for analyzing life insurance and life annuities and determining premiums. This course, together with its sequel, STAT-I 473, provides most of the background for Exams MLC and MFE of the Society of Actuaries.

• STAT-I 473 Actuarial Models II (3 cr.) P: STAT-I 472. Continuation of STAT-I 472. Together, these courses cover contingent payment models, survival models, frequency and severity models, compound distribution models, simulation models, stochastic process models, and ruin models.

• STAT-I 479 Loss Models (3 cr.) P: STAT-I 417 and STAT-I 472 and STAT-I 473. This material provides an introduction to modeling and covers important actuarial methods that are useful in modeling. Students will be introduced to survival, severity, frequency and aggregate models, and use statistical methods to estimate parameters of such models given sample data. The student will further learn to identify steps in the modeling process, understand the underlying assumptions implicit in each family of models, recognize which assumptions are applicable in a given business application, and appropriately adjust the models for impact of insurance coverage modifications. The student will be introduced to a variety of tools for the calibration and evaluation of the models. Permission of instructor required.

• STAT-I 480 Credibility and Simulation (3 cr.) P: STAT-I 479. A continuation of the material covered in STAT-I 479, including Credibility Theory and Simulation calibration and evaluation of the models.

• STAT-I 490 Topics in Statistics for Undergraduates (1-5 cr.) Supervised reading and reports in various fields.

• STAT N501 Statistical Methods for Health Sciences (3 cr.) P: MATH-I 153. An introductory statistical methods course, with emphasis on applications in the health sciences. Topics include descriptive statistics, probability distributions, sampling distributions, confidence interval estimation, hypothesis testing, analysis of variance, linear regression, goodness-of-fit tests, and contingency tables.

• STAT-S 351 Honors Introduction to Statistics (3 cr.) P: MATH-I 166. This course introduces the basic concepts and methods of applied statistics in all areas of science and engineering. Three distinctive features of this honors course are: (1) self-learning and discovery of concepts and methods of statistical analysis through guided instructions, literature search, derivation and simulation; (2) classroom participation - both individually and collaboratively - in active learning of difficult concepts; and (3) communicating such learning to general readers. Students will acquire a basic competence in using statistical freeware R, in presenting data visually, in analyzing data appropriately, in interpreting the results in the context of research problems, and in communicating findings in plain but impactful language to readers not trained in Statistics. STAT-S 351 extends all material covered in STAT-I 350 to a deeper level and gives glimpses into some methodologies delegated to advanced courses, thereby motivating and preparing students to take advanced undergraduate statistics courses.

• STAT-I 414 Introduction to Design of Experiments (3 cr.) P: STAT-I 417 or STAT-I 512 or MATH-M 366 or MATH-M 466 or equivalent. The course offers comprehensive coverage of the key elements of experimental design used by applied researchers to solve problems in the field. It shows students how to use applied statistics for planning, running, and analyzing experiments. The emphasis is placed on the basic philosophy of design. The course requires the use of the software such as SAS, Minitab, or R.

• STAT-S 352 Data Modeling and Inference (3 cr.) P: STAT-I 350 or MATH-M 366 or MATH-M 466 or equivalent. 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 437 Categorical Data Analysis (3 cr.) P: STAT-I 417 or MATH-M 366 or MATH-M 466 or equivalent. 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 460 Sampling (3 cr.) P: STAT-I 417 or MATH-M 366 or MATH-M 466 or equivalent. Design of surveys and analysis of sample survey data. Simple random sampling, ratio and regression estimation, stratified and cluster sampling, complex surveys, nonresponse bias.

Advanced Undergraduate and Graduate

• STAT-I 511 Statistical Methods I (3 cr.) P: MATH-I 165. Descriptive statistics; elementary probability; random variables and their distributions; expectation; normal, binomial, Poisson, and hypergeometric distributions; sampling distributions; estimation and testing of hypotheses; one-way analysis of variance; and correlation and regression.

• STAT-I 512 Applied Regression Analysis (3 cr.) P: STAT-I 511. Inference in simple and multiple linear regression, estimation of model parameters, testing, and prediction. Residual analysis, diagnostics and remedial measures. Multicollinearity. Model building, stepwise, and other model selection methods. Weighted least squares. Models with qualitative independent variables. Analysis of variance. Orthogonal contrasts; multiple comparison tests. Ridge Regression; Lasso Regression.

• STAT-I 513 Statistical Quality Control (3 cr.) P: STAT-I 511. Control charts and acceptance sampling, standard acceptance plans, continuous sampling plans, sequential analysis, and response surface analysis. Use of existing statistical computing packages.

• STAT-I 514 Designs of Experiments (3 cr.) P: STAT-I 512. Fundamentals, completely randomized design, and randomized complete blocks. Latin squares, multiclassification, factorial, nested factorial, balanced incomplete blocks, fractional replications, confounding, general mixed factorial, split-plot, and optimum design. Use of existing statistical computing packages such as R, SAS and Minitab.

• STAT-I 515 Statistical Consulting Problems (1-3 cr.) P: Consent of advisor. Consultation on real-world problems involving statistical analysis under the guidance of a faculty member. A detailed written report and an oral presentation are required.

• STAT-I 516 Basic Probability and Applications (3 cr.) P: MATH-I 261. Instructor consent required for any undergraduate student. A first course in probability intended to serve as a foundation for statistics and other applications. Intuitive background; sample spaces and random variables; joint, conditional, and marginal distributions; special distributions of statistical importance; moments and moment generating functions; statement and application of limit theorems; and introduction to Markov chains.

• STAT-I 517 Statistical Inference (3 cr.) P: STAT-I 511 or STAT-I 516. A basic course in statistical theory covering standard statistical methods and their applications. Includes unbiased, maximum likelihood, and moment estimation; confidence intervals and regions; testing hypotheses for standard distributions and contingency tables; and introduction to nonparametric tests and linear regression.

• STAT-I 519 Introduction to Probability (3 cr.) P: MATH-I 261. Sample spaces and axioms of probability, conditional probability, independence, random variables, distribution functions, moment generating and characteristics functions, special discrete and continuous distributions--univariate and multivariate cases, normal multivariate distributions, distribution of functions of random variables, modes of convergence and limit theorems, including laws of large numbers and central limit theorem.

• STAT-I 520 Time Series and Applications (3 cr.) P: STAT-I 519. A first course in stationary time series with applications in engineering, economics, and physical sciences. Stationarity, autocovariance function and spectrum; integral representation of a stationary time series and interpretation; linear filtering; transfer function models; estimation of spectrum; and multivariate time series. Use of existing statistical computing packages.

• STAT-I 521 Statistical Computing (3 cr.) C: STAT-I 512 or equivalent. A broad range of topics involving the use of computers in statistical methods. SAS and R programming language. Simulation Studies. Bootstrapping. EM algorithm. Machine Learning algorithms.

• STAT-I 522 Sampling and Survey Techniques (3 cr.) P: STAT-I 512 and STAT-I 519. Survey designs and analyses; simple random, stratified, and systematic samples; systems of sampling; methods of estimation; ratio and regression estimates; and costs. Two-stage, multi-stage sampling; Optimization. Other related topics as time permits.

• STAT-I 523 Categorical Data Analysis (3 cr.) P: STAT-I 528, or STAT-I 512 and STAT-I 519. Models generating binary and categorical response data, two-way classification tables, measures of association and agreement, goodness-of-fit tests, testing independence. General linear models, logistic regression, probit and extreme value models, and multinomial logit models. Loglinear models and loglinear-logit Connection. Model building, selection, and diagnostics. Computer applications using existing statistical software.

• STAT-I 524 Applied Multivariate Analysis (3 cr.) P: STAT-I 528. Extension of univariate tests in normal populations to the multivariate case, equality of covariance matrices, multivariate analysis of variance, discriminant analysis and misclassification errors, canonical correlation, principal components, and factor analysis. Strong emphasis on the use of existing computer programs.

• STAT-I 525 Generalized Linear Models (3 cr.) P: STAT-I 528 or equivalent, or consent of instructor. Generalized linear models, likelihood methods for data analysis, and diagnostic methods for assessing model assumptions. Methods covered include multiple regression, analysis of variance for completely randomized designs, binary and categorical response models, and hierarchical loglinear models for contingency tables.

• STAT-I 528 Mathematical Statistics (3 cr.) P: STAT-I 519. Sufficiency and completeness, the exponential family of distributions, theory of point estimation, Cramer-Rao inequality, Rao-Blackwell Theorem with applications, maximum likelihood estimation, asymptotic distributions of ML estimators, hypothesis testing, Neyman-Pearson Lemma, UMP tests, generalized likelihood ratio test, asymptotic distribution of the GLR test, and sequential probability ratio test.

• STAT-I 529 Applied Decision Theory and Bayesian Analysis (3 cr.) P: STAT-I 528 or equivalent. Foundation of statistical analysis, Bayesian and decision theoretic formulation of problems; construction of utility functions and quantifications of prior information; methods of Bayesian decision and inference, with applications; empirical Bayes; combination of evidence; and game theory and minimax rules, Bayesian design, and sequential analysis. Comparison of statistical paradigms.

• STAT-I 532 Elements of Stochastic Processes (3 cr.) P: STAT-I 519. A basic course in stochastic models including discrete and continuous time processes, Markov chains, and Brownian motion. Introduction to topics such as Gaussian processes, queues and renewal processes, and Poisson processes. Application to economic models, epidemic models, and reliability problems.

• STAT-I 533 Nonparametric Statistics (3 cr.) P: STAT-I 516 or consent of instructor. Binomial test for dichotomous data, confidence intervals for proportions, order statistics, one-sample signed Wilcoxon rank test, two-sample Wilcoxon test, two-sample rank tests for dispersion, and Kruskal-Wallis test for one-way layout. Runs test and Kendall test for independence, one- and two-sample Kolmogorov-Smirnov tests, and nonparametric regression.

• STAT-I 536 Introduction to Survival Analysis (3 cr.) P: STAT-I 517 or equivalent. Deals with the modern statistical methods for analyzing time-to-event data. Background theory is provided, but the emphasis is on the applications and the interpretations of results. Provides coverage of survivorship functions and censoring patterns; parametric models and likelihood methods, special life-time distributions; nonparametric inference, life tables, estimation of cumulative hazard functions, and the Kaplan-Meier estimator; one- and two-sample nonparametric tests for censored data; and semiparametric proportional hazards regression (Cox Regression), parameters' estimation, stratification, model fitting strategies, and model interpretations. Heavy use of statistical software such as R and SAS.

• STAT-I 598 Topics in Statistical Methods (0-6 cr.) P: Consent of instructor. Directed study and reports for students who wish to undertake individual reading and study on approved topics.

• STAT-I 619 Probability Theory (3 cr.) P: STAT-I 519. Probability Theory is the foundation of statistical methodologies, which is fundamental in the practice of science. From this course students will get a precise mathematical understanding of probabilities and sigma-algebras, random weak convergence, characteristic functions, the central limit theorem, Lebesgue decomposition, conditioning and martingales.

• STAT-I 628 Advanced Statistical Inference (3 cr.) P: STAT-I 519 and STAT-I 528. C: STAT-I 619. Real analysis for inference, statistics and subfields, conditional expectations and probability distributions, UMP tests with applications to normal distributions and confidence sets, invariance, asymptotic theory of estimation and likelihood based inference, U-statistics, Edgeworth expansions, saddle point method.

• STAT-I 698 Research M.S. Thesis (6 cr.) P: Consent of advisor. M.S. thesis in Applied Statistics.