Organized study of selected topics. Subjects and earnable credit may vary from semester to semester. Repeatable with departmental consent.
Credit Hour: 1-99
Prerequisites: instructor's consent
Basic inference methods, both parametric and non-parametric, appropriate for answering questions arising in health sciences research. Computer exercises involving data from real experiments from health science area.
Credit Hours: 3
Prerequisites: MATH 1100 or MATH 1120 and instructor's consent
Primarily for middle and secondary mathematics education majors. Uses standards-based curricular materials to demonstrate connections between college-level statistics and content taught in middle and secondary schools. No credit toward a graduate degree in statistics.
Credit Hours: 3
Prerequisites: an introductory course in statistics or MATH 2320 or instructor's consent
Designed for graduate students who have no previous training in statistics. Topics include descriptive statistics, probability distributions, estimation, hypothesis testing, regression, and ANOVA. No credit toward a degree in statistics.
Credit Hours: 3
Prerequisites: either MATH 1100 or MATH 1120
Approved reading and study, independent investigations, and reports on approved topics.
Credit Hour: 1-99
Prerequisites: instructor's consent
Programming with major statistical packages emphasizing data management techniques and statistical analysis for regression, analysis of variance, categorical data, descriptive statistics, non-parametric analyses, and other selected topics.
Credit Hours: 3
Prerequisites: STAT 3500, STAT 7070, STAT 4710 or STAT 7710, STAT 4760 or STAT 7760, or instructor's consent
The study of statistical models and methods used in analyzing categorical data. The use of computing is emphasized and calculus is not required. No credit for students who have previously completed STAT 4830. No credit toward a graduate degree in statistics.
Credit Hours: 3
Prerequisites: STAT 3500, STAT 7070, STAT 4710 or STAT 7710, or STAT 4760 or STAT 7760 or instructor's consent
Statistical methods when the functional form of the population is unknown. Applications emphasized. Comparisons with parametric procedures. Goodness of-fit, chi-square, comparison of several populations, measures of correlation.
Credit Hours: 3
Prerequisites: STAT 3500, STAT 7070, STAT 4710 or STAT 7710, STAT 4760 or STAT 7760, or instructor's consent
Theory of probability sampling designs. Unrestricted random sampling. Stratified sampling. Cluster sampling. Multistage or subsampling. Ratio estimates. Regression estimates. Double sampling.
Credit Hours: 3
Prerequisites: STAT 3500, STAT 7070, STAT 4710 or STAT 7710, STAT 4760 or STAT 7760, or instructor's consent
Introductory course on collecting, processing, visualizing, and analyzing data in sports. Technologies used in data collection and processing will be explored, along with methods for measuring and comparing individual and team performance.
Credit Hours: 3
Prerequisites: Any one of STAT 3500, STAT 7020, STAT 7070, STAT 4710/7710, STAT 4760/7760, or instructor's consent
Advanced course in methods for analyzing individual and team based performance in sports and the use of data to drive strategy and tactics. Emphasis will be put on analytical methods to improve skills and optimize the performance of athletes.
Credit Hours: 3
Prerequisites: Both STAT 4330 or STAT 7330 and STAT 4510 or STAT 7510
(cross-leveled with STAT 4410). Study of statistical techniques for the design and analysis of clinical trials, laboratory studies and epidemiology. Topics include randomization, power and sample size calculation, sequential monitoring, carcinogenicty bioassay and case-cohort designs. Prerequisites: any of the following: STAT 3500, STAT 7070, STAT 4710, STAT 7710, STAT 4760, STAT 7760, or instructor's consent.
Credit Hours: 3
Parametric models; Kaplan-Meier estimator; nonparametric estimation of survival and cumulative hazard functions; log-rank test; Cox model; Stratified Cox model; additive hazards model partial likelihood; regression diagnostics; multivariate survival data.
Credit Hours: 3
Prerequisites: STAT 3500, STAT 7070, STAT 4710 or STAT 7710 or STAT 4760 or STAT 7760 or instructor's consent
Repeated measurements; event history studies; linear and nonlinear mixed effects models; growth models; marginal mean and rate models; pattern-mixture models; selection models; non-informative and informative drop-out; joint analysis of longitudinal and survival data.
Credit Hours: 3
Prerequisites: STAT 3500, STAT 7070, STAT 4710 or STAT 7710, or STAT 4760 or STAT 7760 or instructor's consent
Random variables; Point estimation; Multiple t-test; Likelihood principle; Analysis of variance; Probabilistic methods for sequence modeling; Gene expression analysis; Protein structure prediction; Genome analysis; Hierarchical clustering and Gene classification.
Credit Hours: 3
Prerequisites: STAT 3500, 7070, STAT 4710 or STAT 7710, STAT 4760 or STAT 7760, or instructor's consent
Introduction to applied statistical models including regression and ANOVA, logistic regression, discriminant analysis, tree-based methods, semi-parametric re- gression, support vector machines, and unsupervised learning through principal component analysis and clustering. No credit toward a graduate degree in statistics.
Credit Hours: 3
Prerequisites: STAT 3500, STAT 7070, STAT 4710 or STAT 7710, STAT 4760 or STAT 7760, or instructor's consent
Advanced course in applied statistical modeling focusing on extensions of the linear model. Topics include generalized linear models, such as logistic and Poisson regression. Random effects models will also be introduced, with emphasis on linear and generalized linear mixed models, repeated measures, and longitudinal data. These methods will extend to general models for dependent data, such as spatially-referenced data and time series. Lastly, nonlinear models through neural networks and deep learning will also be discussed.
Credit Hours: 3
Prerequisites: STAT 4510 or STAT 7510 or instructor's consent
Study of analysis of variance and related modeling techniques for cases with fixed, random, and mixed effects. Exposure to designs other than completely randomized designs including factorial arrangements, repeated measures, nested, and unequal sample size designs.
Credit Hours: 3
Prerequisites: STAT 3500, STAT 7070, STAT 4710 or STAT 7710, STAT 4760 or STAT 7760, or instructor's consent
(cross-leveled with STAT 4580). Introduction to basic concepts of and statistical methods used in customized pricing. Focuses on applying statistical methods to real customized pricing problems. Students will gain an understanding of customized pricing and some hands on experience with SAS Enterprise minor.
Credit Hours: 3
Prerequisites: STAT 3500 or STAT 4510 or STAT 7510 or instructor's consent
Study of random processes selected from: Markov chains, birth and death processes, random walks, Poisson processes, renewal theory, Brownian motion, Gaussian processes, white noise, spectral analysis, applications such as queuing theory, sequential tests.
Credit Hours: 3
Prerequisites: STAT 4750 or STAT 7750 or instructor's consent
A study of univariate and multivariate time series models and techniques for their analyses. Emphasis is on methodology rather than theory. Examples are drawn from a variety of areas including business, economics and soil science.
Credit Hours: 3
Prerequisites: STAT 7710 or STAT 7760 or instructor's consent
Approved reading and study, independent investigations, and reports on approved topics.
Credit Hour: 1-99
Prerequisites: instructor's consent
Graded on a S/U basis only.
Credit Hour: 1-99
Credit Hour: 1-99
Prerequisites: instructor's consent
Advanced use of R and Python for data analysis, including statistical programming, data manipulation, database operations, and interfacing between languages. Course will provide critical tools for the practice and application of statistics to data from a wide variety of fields using up-to-date tooling.
Credit Hours: 3
Recommended: Prior completion of the equivalent of at least two undergraduate statistics courses
Applications of linear models including regression (simple and multiple, subset selection, regression diagnostics), analysis of variance (fixed, random and mixed effects, contrasts, multiple comparisons) and analysis of covariance; alternative nonparametric methods.
Credit Hours: 3
Prerequisites: STAT 4710 or STAT 7710 or STAT 4760 or STAT 7760 or instructor's consent
Advanced applications including analysis of designs (e.g. repeated measures, hierarchical models, missing data), multivariate analysis (Hotelling's T2, MANOVA, discriminant analysis, principal components, factor analysis), nonlinear regression, generalized linear models, categorical data analysis.
Credit Hours: 3
Prerequisites: STAT 8310 or instructor's consent
An introduction to data analysis techniques associated with supervised and unsupervised statistical learning. Resampling methods, model selection, regularization, generalized additive models, trees, support vector machines, clustering, nonlinear dimension reduction.
Credit Hours: 3
Prerequisites: STAT 8320
Participation in statistical consulting under faculty supervision. Formulation of statistical problems. Planning of surveys and experiments. Statistical computing. Data analysis. Interpretation of results in statistical practice.
Credit Hours: 3
Prerequisites: STAT 4760 or STAT 7760 and STAT 8320 or instructor's consent
Bayes' theorem, subjective probability, non-informative priors, conjugate prior, asymptotic properties, model selection, computation, hierarchical models, hypothesis testing, inference, predication, applications.
Credit Hours: 3
Prerequisites: STAT 4760 or STAT 7760 and MATH 4140 or MATH 7140 or instructor's consent
Further development of estimation theory, including sufficiency, minimum variance principles and Bayesian estimation. Tests of hypotheses, including uniformly most powerful and likelihood ratio tests.
Credit Hours: 3
Prerequisites: STAT 8710 or instructor's consent
Approved reading and study, independent investigations, and reports on approved topics.
Credit Hour: 1-99
Prerequisites: instructor's consent
Graded on a S/U basis only.
Credit Hour: 1-99
The content of the course which varies from semester to semester, will be the study of some statistical theories or methodologies which are currently under development, such as bootstrapping, missing data, non-parametric regression, statistical computing, etc.
Credit Hours: 3
Prerequisites: STAT 4760 or STAT 7760 and instructor's consent
Theory of multiple regression and analysis of variance including matrix representation of linear models, estimation, testing hypotheses, model building, contrasts, multiple comparisons and fixed and random effects.
Credit Hours: 3
Prerequisites: STAT 4760 or STAT 7760 and MATH 4140 or MATH 7140, and instructor's consent
Advanced topics in the theory and application of linear models. Specific content varies with instructor.
Credit Hours: 3
Prerequisites: STAT 9310 or instructor's consent
Distribution of sample correlation coefficients. Derivation of generalized T-squared and Wishart distributions. Distribution of certain characteristic roots, vectors. Test of hypotheses about covariance matrices and mean vectors. Discriminant analysis.
Credit Hours: 3
Prerequisites: STAT 4760 or STAT 7760 and MATH 4140 or MATH 7140 or instructor's consent
Approaches to estimating unspecified relationships and findings unexpected patterns in high dimensional data. Computationally intensive methods including splines, classifications, tree-based and bagging methods, support vector machines.
Credit Hours: 3
Prerequisites: STAT 4110 or STAT 7110, STAT 4760 or STAT 7760 and STAT 8320 or instructor's consent
Advanced study of mathematical statistics appropriate for PhD students in statistics. Elements of probability theory, principles of data reduction, point and interval estimation, methods of finding estimators and their properties, hypothesis testing, methods of finding test functions and their properties. Decision theoretic, classical and Bayesian perspectives.
Credit Hours: 3
Prerequisites: STAT 8720 or instructor's consent
(same as MATH 8480). Measure theoretic probability theory. Characteristic functions; conditional probability and expectation; sums of independent random variables including strong law of large numbers and central limit problem.
Credit Hours: 3
Prerequisites: STAT 4750 STAT 7750 or MATH 4700 or MATH 7700 or instructor's consent
Chong (Zhuoqiong) He
Professor and Director of Graduate Studies
hezh@missouri.edu
General Inquiries:
umcasstat@missouri.edu