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

Examination and analysis of modern statistical techniques applicable to experimentation in social, physical or biological sciences.

Credit Hours: 3
Prerequisites: STAT 3500 or STAT 4510 or STAT 7510 or STAT 4530 or STAT 7530 or instructor's consent

Testing mean vectors; discriminant analysis; principal components; factor analysis; cluster analysis; structural equation modeling; graphics.

Credit Hours: 3
Prerequisites: STAT 3500, STAT 7070, STAT 4710 or STAT 7710 or STAT 4760 or STAT 7760. No credit toward a graduate degree in statistics

(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

Introduction to spatial random processes, spatial point patterns, kriging, simultaneous and conditional autoregression, and spatial data analysis.

Credit Hours: 3
Prerequisites: STAT 4510 or STAT 7510 or instructor's consent
Recommended: Basic knowledge of calculus and matrices

Bayes formulas, choices of prior, empirical Bayesian methods, hierarchal Bayesian methods, statistical computation, Bayesian estimation, model selection, predictive analysis, applications, Bayesian software.

Credit Hours: 3
Prerequisites: STAT 3500 or STAT 4510 or STAT 7510 or instructor's consent

(same as MATH 7315). Introduction to theory of probability and statistics using concepts and methods of calculus.

Credit Hours: 3
Prerequisites: MATH 2300 or instructor's consent. No credit MATH 7315

(same as MATH 7320). Probability spaces; random variables and their distributions; repeated trials; probability limit theorems.

Credit Hours: 3
Prerequisites: MATH 2300 or instructor's consent

(same as MATH 7520). Sampling; point estimation; sampling distribution; tests of hypotheses; regression and linear hypotheses.

Credit Hours: 3
Prerequisites: STAT 4750 or STAT 7750 or instructor's consent

A first course in Non-parametric statistical methods based on ranks. Both theory and application are emphasized. Two-sample problems. K-sample problems. Tests for independence. Contingency tables. Goodness-of-fit tests.

Credit Hours: 3
Prerequisites: STAT 4710 or STAT 7710 or instructor's consent

Discrete distributions, frequency data, multinomial data, chi-square and likelihood ratio tests, logistic regression, log linear models, rates, relative risks, random effects, case studies.

Credit Hours: 3
Prerequisites: STAT 4710 or STAT 7710 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

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

Study of statistical theory and methods underpinning bioinformatics. Topics include statistical theory used in biotechnologies such as gene sequencing, gene alignments, microarrays, phylogentic trees, evolutionary models, proteomics and imaging.

Credit Hours: 3
Prerequisites: STAT 4760 or STAT 7760

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

Sample spaces, probability and conditional probability, independence, random variables, expectation, distribution theory, sampling distributions, laws of large numbers and asymptotic theory, order statistics.

Credit Hours: 3
Prerequisites: STAT 4760 or STAT 7760 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

Random number generation, acceptance/rejection methods; Monte Carlo; Laplace approximation; the EM algorithm; importance sampling; Markov chain Monte Carlo; Metropolis-Hasting algorithm; Gibbs sampling, marginal likelihood.

Credit Hours: 3
Prerequisites: STAT 4760 or STAT 7760 or 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

Statistical failure models, Kaplan-Meier estimator, Log-rank test, Cox's regression model, Multivariate failure time date analysis, Counting process approaches.

Credit Hours: 3
Prerequisites: STAT 4760 or STAT 7760 or instructor's consent

Estimation, hypothesis testing, confidence intervals, etc., when functional form of the population distribution is unknown.

Credit Hours: 3
Prerequisites: STAT 4760 or STAT 7760 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

Likelihood principle, decision theory, asymptotic properties, advanced topics in Bayesian analysis at the instructor's discretion.

Credit Hours: 3
Prerequisites: STAT 8640 and STAT 9710 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

Continuation of STAT 9710. Topics include distribution theory and convergence, laws of large numbers, central limit theorems, efficiency, large sample theory, and elements of advanced probability.

Credit Hours: 3
Prerequisites: STAT 9710 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

(same as MATH 8680). Markov processes, martingales, orthogonal sequences, processes with independent and orthogonal increments, stationarity, linear prediction.

Credit Hours: 3
Prerequisites: STAT 9810 or instructor's consent

Contact Info

Lori Thombs
Associate Professor and Director of Graduate Studies 
ThombsL@Missouri.edu

General Inquiries:
umcasstat@missouri.edu