IUPUI Bulletins » Schools » purdue-science » Courses » Statistics


  • STAT 19000 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 29000 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 30100 Elementary Statistical Methods I (3 cr.) P: MATH 11000 or 11100 taken within last 3 terms with a grade of C or better 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 35000 Introduction to Statistics (3 cr.) P: MATH 16600. 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 51100 but with emphasis on more data-analytic material. Includes a weekly computing laboratory using Minitab.
  • STAT 37100 Prep for Actuarial Exam I (2 cr.) This course is intended to help actuarial students prepare for the SOA/CAS Exam P/1.
  • STAT 39000 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 41600 Probability (3 cr.) P: MATH 26100. 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 41700 Statistical Theory (3 cr.) P: 41600. C: 35000. 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 47200 Actuarial Models I (3 cr.) P: 41700 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 47300, provides most of the background for Exams MLC and MFE of the Society of Actuaries.
  • STAT 47300 Actuarial Models II (3 cr.) P: 47200. Continuation of 47200. 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 47900 Loss Models (3 cr.) P: STAT 41700 and STAT 47200 and STAT 47300. 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 49000 Topics in Statistics for Undergraduates (1-5 cr.) Supervised reading and reports in various fields.
  • STAT 42100 Modern Statistical Modeling Using R and SAS (3 cr.) P: STAT 41700 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 43200 Introduction to Stochastic Process and Probability Modeling (3 cr.) P: STAT 41600 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 43301 Introduction to Nonparametric Statistics (3 cr.) P: STAT 41700 and STAT 42100 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 51100, 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 48000 Credibility and Simulation (3 cr.) P: STAT 47900 A continuation of the material covered in STAT 47900, including Credibility Theory and Simulation calibration and evaluation of the models.
  • STAT N501 Statistical Methods for Health Sciences (3 cr.) P: MATH 15300 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.
Advanced Undergraduate and Graduate
  • STAT 51100 Statistical Methods I (3 cr.) P: MATH 16500. 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 51200 Applied Regression Analysis (3 cr.) P: 51100. 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. Nonlinear regression. Models with qualitative independent variables. One-way analysis of variance. Orthogonal contrasts and multiple comparison tests. Use of existing statistical computing package.
  • STAT 51300 Statistical Quality Control (3 cr.) P: 51100. Control charts and acceptance sampling, standard acceptance plans, continuous sampling plans, sequential analysis, and response surface analysis. Use of existing statistical computing packages.
  • STAT 51400 Designs of Experiments (3 cr.) Fundamentals, completely randomized design, and randomized complete blocks. Latin squares, multiclassification, factorial, nested factorial, incom-plete blocks, fractional replications, confounding, general mixed factorial, split-plot, and optimum design. Use of existing statistical computing packages.
  • STAT 51500 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 51600 Basic Probability and Applications (3 cr.) P: MATH 26100. 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 51700 Statistical Inference (3 cr.) P: 51100 or 51600. 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 51900 Introduction to Probability (3 cr.) P: MATH 26100. 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 52000 Time Series and Applications (3 cr.) P: 51900. 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 52100 Statistical Computing (3 cr.) C: 51200 or equivalent. A broad range of topics involving the use of computers in statistical methods. Collection and organization of data for statistical analysis; transferring data between statistical applications and computing platforms; techniques in exploratory data analysis; and comparison of statistical packages.
  • STAT 52200 Sampling and Survey Techniques (3 cr.) P: 51200. Survey designs; simple random, stratified, and systematic samples; systems of sampling; methods of estimation; ratio and regression estimates; and costs. Other related topics as time permits.
  • STAT 52300 Categorical Data Analysis (3 cr.) P: 52800. Models generating binary and categorical response data, two-way classification tables, measures of association and agreement, goodness-of-fit tests, testing independence, large sample properties. General linear models, logistic regression, and probit and extreme value models. Loglinear models in two and higher dimensions; maximum likelihood estimation, testing goodness-of-fit, partitioning chi-square, and models for ordinal data. Model building, selection, and diagnostics. Other related topics as time permits. Computer applications using existing statistical software.
  • STAT 52400 Applied Multivariate Analysis (3 cr.) 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 52500 Intermediate Statistical Methodology (3 cr.) C: STAT 52800 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 52501 Generalized Linear Models (3 cr.) P: 52800 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 52800 Mathematical Statistics (3 cr.) P: 51900. 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 52900 Applied Decision Theory and Bayesian Analysis (3 cr.) P: STAT 52800. 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.
  • MATH 53200 Elements of Stochastic Processes (3 cr.) P: 51900. 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 53300 Nonparametric Statistics (3 cr.) P: 51600. 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 53600 Introduction to Survival Analysis (3 cr.) P: 51700. 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 Splus and SAS.
  • STAT 59800 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 61900 Probability Theory (3 cr.) P: STAT 51900 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, Lobesgue decomposition, conditioning and martingales.
  • STAT 62800 Advanced Statistical Inference (3 cr.) P: STAT 51900, 52800, C: STAT 61900. 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 69800 Research M.S. Thesis (6 cr.) P: Consent of advisor. M.S. thesis in Applied Statistics.