Courses

View the current course schedule.

Undergraduate Courses

STAT 100. Statistical Literacy in the Age of Information.

Credits: 3
Focus will be on the development of an awareness of statistics at the conceptual and interpretative level, in the context of everyday life. Data awareness and quality, sampling, scientific investigation, decision making, and the study of relationships are included. Emphasis will be on the development of critical thinking through in-class experiments and activities, discussions, analysis of real data sets, written reports, and collaborative learning. Computing activities will be included where appropriate; no previous computing experience required.
Notes:
Intended for majors in non-quantitative fields. Cannot be taken for credit if credit has been received for any other statistics course.
Repeat for Credit
N
Typically Offered
Fall, Spring
K-State 8
Empirical and Quantitative Reasoning

STAT 150. Statistics and Data Science: Introductory Seminar for Majors.

Credits: 1
An introduction to the field of statistics and data science. Topics of special interest to undergraduates majoring in statistics and data sciences, including cultural and professional aspects of the field and exploration of careers available to those trained in statistics and data science.
Repeat for Credit
N
Typically Offered
Fall, Spring

STAT 225. Introduction to Statistics.

Credits: 3
A first course in probability and statistics; random sampling; graphical and numeric summaries of qualitative and quantitative data; correlation and least squares regression; probability and random variables; the normal distribution, sampling distributions and the Central Limit Theorem; confidence intervals for one mean and one proportion; sample size estimation; hypothesis tests for one parameter, power and type I and II errors; statistical inference for the difference of two means based on independent samples and matched pairs designs; statistical inference for the difference of two population proportions.
Note:
Cannot be taken for credit if credit has been received for STAT 240 or 250.
Repeat for Credit
N
Requisites:
Students not meeting at least one of the KBOR placement measures below are required to also take STAT 226, which is a 2-credit corequisite support class.
Placement measures: A test score equal to or greater than, Math ACT 19 OR Math SAT 510 OR ALEKS PPL 30 OR Accuplacer QAS 255; OR (unweighted HS GPA 3.00 AND grade of C- or better in Algebra 2 or Integrated Math 3); OR MATH 100.
Typically Offered
Fall, Spring, Summer
K-State 8
Empirical and Quantitative Reasoning

STAT 226. Introduction to Statistics Support.

Credits: 2

Note: Introduction to Statistics Support credits cannot count towards the 120 required credits of a degree program. Students who do not meet any of the KBOR multiple placement measures for elementary statistics must also enroll in STAT 226 as the corequisite course to STAT 225.

Introduction to Statistics Support is the corequisite course for STAT 225 -Introduction to Statistics. STAT 226 aims to fill in gaps in students’ background knowledge to promote success in STAT 225. The content of STAT 226 is directly aligned with the course objectives and content of STAT 225 but allows for flexibility to fit individual student needs. Topics vary based on student needs but can include simplifying and manipulating numerical and algebraic expressions; and reviewing functions, plots, graphs of functions, exponents, logarithms, and set theory. Students must enroll concurrently in STAT 225 and STAT 226.

This course was designed through a Kansas Board of Regents Math Pathways initiative. STAT 226 is the KBOR math pathways corequisite course for the Introduction to Statistics course.

Repeat for Credit
N
Requisites:
Corequisite: STAT 225
Typically Offered
Fall, Spring

STAT 240. Biometrics I.

Credits: 3
A basic first course in probability and statistics with textbook, examples, and problems aimed toward the biological sciences. Frequency distributions, averages, measures of variation, probability, confidence intervals; tests of significance appropriate to binomial, multinomial, Poisson, and normal sampling; simple regression and correlation.
Note:
Cannot be taken for credit if credit has been received for STAT 225 or 250.
Repeat for Credit
N
Typically Offered
Fall, Spring, Summer
K-State 8
Empirical and Quantitative Reasoning

STAT 250. Business and Economic Statistics I.

Credits: 3
A basic first course in probability and statistics with textbook, examples, and problems pointed toward business administration and economics. Frequency distributions, averages, index numbers, time series, measures of variation, probability, confidence intervals, tests of significance appropriate to binomial, multinomial, Poisson, and normal sampling; simple regression and correlation.
Note:
Cannot be taken for credit if credit has been received for STAT 225 or 240.
Repeat for Credit
N
Typically Offered
Spring
K-State 8
Empirical and Quantitative Reasoning

STAT 341. Biometrics II.

Credits: 3
Analysis and interpretation of biological data using analysis of variance, analysis of covariance, and multiple regression. Negative binomial distribution and its applications.
Repeat for Credit
N
Requisites:
Prerequisite: STAT 225 or STAT 240 or STAT 250.
Typically Offered
Spring

STAT 351. Business and Economic Statistics II.

Credits: 3
Continuation of STAT 250 including study of index numbers, time series, business cycles, seasonal variation, multiple regression and correlation, forecasting; some nonparametric methods applicable in business and economic studies.
Repeat for Credit
N
Requisites:
Prerequisite: STAT 225, STAT 240 or STAT 250.
Typically Offered
Fall, Spring, Summer

STAT 410. Probabilistic Systems Modeling.

Credits: 3
Descriptive statistics and graphical methods; basic probability; probability distributions; several random variable; Poisson processes; computer simulation of random phenomena; confidence interval estimation; hypothesis testing.
Repeat for Credit
N
Requisites:
Prerequisite: MATH 221 and CIS 300.
Typically Offered
Spring

STAT 450. Special Topics for Undergraduates in Statistics and Data Science.

Credits: 1-3
Study of special topics in statistics and data science methods for undergraduates.
Note:
Instructor consent
Repeat for Credit
Y
Typically Offered
Fall, Spring, Summer

STAT 499. Honors Project.

Credits: 3
Open only to Arts and Science students who are active members of the University Honors Program.
Repeat for Credit
N
Typically Offered
Fall, Spring, Summer

STAT 510. Introductory Probability and Statistics I.

Credits: 3
Descriptive statistics, probability concepts and laws, sample spaces; random variables; binomial, uniform, normal, and Poisson; two-dimensional variates; expected values; confidence intervals; binomial parameter, median, mean, and variance; testing simple hypotheses using CIs and X2; goodness of fit. Numerous applications.
Repeat for Credit
N
Requisites:
Prerequisite: MATH 221.
Typically Offered
Fall, Spring

STAT 511. Introductory Probability and Statistics II.

Credits: 3
Law of Large Numbers, Chebycheff’s Inequality; continuation of study of continuous variates; uniform, exponential, gamma, and beta distribution; Central Limit Theorem; distributions from normal sampling; introduction to statistical inference.
Repeat for Credit
N
Requisites:
Prerequisite: STAT 510.
Typically Offered
Spring

Undergraduate and Graduate Courses

STAT 610. Introduction to Mathematical Statistics I

Credits: 3
Development of axiomatic probability; univariate and multivariate random variables and their probability distribution functions; conditional distributions and independent random variables; methods of transformation for distributions of functions of random variables; convergence in distribution and probability.
Repeat for Credit
N
Requisites:
Prerequisite: MATH 222.
Typically Offered
Fall

STAT 611 - Introduction to Mathematical Statistics II

Credits: 3
Estimation of probability distribution parameters by maximum likelihood, Bayesian and bootstrap methods; minimum variance unbiased estimation based on sufficient statistics; mean squared error and consistency of estimators; confidence interval estimation; statistical tests of hypotheses by likelihood ratios; most powerful tests; distribution-free inference; applications to regression models and categorical data.
Repeat for Credit
N
Requisites:
Prerequisite: STAT 610.
Typically Offered
Spring

STAT 701. Fundamental Methods of Biostatistics.

Credits: 3
A course emphasizing concepts and practice of statistical data analysis for the health sciences. Basic techniques of descriptive and inferential statistical methods applied to health related surveys and designed experiments. Populations and samples, parameters and statistics; sampling distributions for hypothesis testing and confidence intervals for means and proportions involving one sample, paired samples and multiple independent samples; odds ratios, risk ratios, simple linear regression. Use of statistical software to facilitate the collection, manipulation, analysis and interpretation of health related data.
Repeat for Credit
N
Typically Offered
Fall, Spring, Summer
Crosslisted:
MPH 701

STAT 703. Introduction to Statistical Methods for the Sciences.

Credits: 3
Statistical concepts and methods applied to experimental and survey research in the sciences; tests of hypotheses, parametric and rank tests; point estimation and confidence intervals; linear regression; correlation; one-way analysis of variance; contingency tables, chi-square tests.
Repeat for Credit
N
Requisites:
Prerequisite: Junior standing and equivalent of college algebra.
Typically Offered
Fall, Spring, Summer

STAT 705. Regression and Analysis of Variance.

Credits: 3
Simple and multiple linear regression, analysis of covariance, correlation analysis, one-, two- and three-way analysis of variance; multiple comparisons; applications including use of computers; blocking and random effects.
Repeat for Credit
N
Requisites:
Prerequisite: One previous statistics course.
Typically Offered
Fall, Spring, Summer

STAT 706. Basic Elements of Statistical Theory.

Credits: 3
The mathematical representation of frequency distributions, their properties, and the theory of estimation and hypothesis testing. Elementary mathematical functions are used to illustrate theory.
Repeat for Credit
N
Requisites:
Prerequisite: MATH 205, MATH 210 or MATH 220 and STAT 225 or equivalent.
Typically Offered
Fall

STAT 710. Sample Survey Methods.

Credits: 3
Design, conduct, and interpretation of sample surveys.
Repeat for Credit
N
Requisites:
Prerequisite: STAT 510 or STAT 770.
Typically Offered
Fall-Even Years

STAT 713. Applied Linear Statistical Models.

Credits: 3
Matrix-based, applied linear regression procedures at a mathematical level appropriate for a first-year graduate statistics major. Topics include basic methodological development for simple and multiple linear regression, model building and diagnostics, analysis of variance/covariance and multiple comparison methods. Other topics from ANOVA modeling may also be covered if time permits.
Note:
A student may not receive graduate credit for both STAT 705 and STAT 713.
Repeat for Credit
N
Requisites:
Prerequisite: MATH 515/551 or an equivalent course and one prior course in statistics.
Typically Offered
Fall

STAT 716. Nonparametric Statistics.

Credits: 3
Hypothesis testing when form of population sampled is unknown: rank, sign, chi-square, and slippage tests; Kolmogorov and Smirnov type tests; confidence intervals and bands.
Repeat for Credit
N
Requisites:
Prerequisite: STAT 705 or STAT 713.
Typically Offered
Fall-Odd Years

STAT 717. Categorical Data Analysis.

Credits: 3
Analysis of categorical count and proportion data. Topics include tests of association in two-way tables; measures of association; Cochran-Mantel-Haenzel tests for 3-way tables; generalized linear models; logistic regression; loglinear models.
Repeat for Credit
N
Requisites:
Prerequisite: STAT 705 or STAT 713.
Typically Offered
Spring

STAT 720. Design of Experiments.

Credits: 3
Planning experiments so as to minimize error variance and avoid bias; Latin squares; split-plot designs; switch-back or reversal designs; incomplete block designs; efficiency.
Repeat for Credit
N
Requisites:
Prerequisite: STAT 705 or STAT 713.
Typically Offered
Spring, Summer

STAT 722. Experimental Designs for Product Development and Quality Improvement.

Credits: 3
A study of statistically designed experiments which have proven to be useful in product development and quality improvement. Topics include randomization, blocking, factorial treatment structures, factional factorial designs, screening designs, and response surface methods.
Repeat for Credit
N
Requisites:
Prerequisite: STAT 705 or STAT 511 or STAT 713.
Typically Offered
Fall

STAT 725. Introduction to SAS Computing.

Credits: 1
Topics may include basic environment and syntax, reading and importing data from files, writing and exporting data to files, data manipulation, basic graphics, and built-in and user-defined functions.
Repeat for Credit
N
Requisites:
Prerequisite: One prior course in Statistics.
Typically Offered
Fall

STAT 726. Introduction to R Computing.

Credits: 1
Topics may include basic environment and syntax, reading and importing data from files, data manipulation, basic graphics, and built-in and user-defined functions.
Repeat for Credit
N
Requisites:
Prerequisite: One prior level course in Statistics.
Typically Offered
Fall

STAT 727. Statistical Computing/Numerical Methods of Statistics.

Credits: 3
Topics may include efficient programming techniques, generating data from non-standard distributions, designing simulation studies, resampling methods, creating and using functions and subroutines, parallel computing in statistics, interfacing lower-level languages (e.g., C, C++, Fortran) with data analysis software (e.g., SAS, R), computational complexity, convergence analysis of algorithms.
Repeat for Credit
N
Requisites:
Prerequisite: STAT 726 and STAT 771 or STAT 511.
Typically Offered
Spring

STAT 730. Multivariate Statistical Methods.

Credits: 3
Multivariate analysis of variance and covariance; classification and discrimination; principal components and introductory factor analysis; canonical correlation; digital computing procedures applied to data from natural and social sciences.
Repeat for Credit
N
Requisites:
Prerequisite: STAT 705 or STAT 713.
Typically Offered
Spring

STAT 736. Bioassay.

Credits: 2
Direct assays; quantitative dose-response models; parallel line assays; slope ratio assays; experimental designs for bioassay; covariance adjustment; weighted estimates; assays based on quantal responses.
Note:
Meets four times a week during second half of semester.
Repeat for Credit
N
Requisites:
Prerequisite: STAT 705 or STAT 713.
Typically Offered
Spring-Odd Years

STAT 745. Statistical Graphics.

Credits: 3
Visual display of quantitative information. Statistical graphics topics to include visual perception, basic graphics construction, quantitative univariate to multivariate statistical graphics, trellis displays, introduction to smoothing and graphics, introduction to density estimation and graphics, and categorical graphics. Modern graphics software will be used.
Repeat for Credit
N
Requisites:
Prerequisite: STAT 705 or equivalent.
Typically Offered
Spring-Even Years

STAT 750. Studies in Probability and Statistics

Credits: 1-4
Studies of topics in probability, statistics, experimental design, stochastic processes, or other topics.
Note:
Instructor consent
Repeat for Credit
Y
Typically Offered
On sufficient demand

STAT 760. Optimization for Data Science.

Credits: 3
The class presents the theory and algorithms for linear and nonlinear optimization problems with continuous variables. Topics covered include convex analysis, first- and second-order optimality methods, duality, KKT conditions, algorithms for unconstrained optimization, linearly and nonlinearly constrained problems, and convergence rates of algorithms. The theory and algorithms are applied to data science problems arising in machine learning, statistics, and related fields (e.g., maximizing likelihood and penalized likelihood functions, MM algorithms, EM algorithms, model selection, Sparse PCA). Students are expected to be comfortable with rigorous mathematical arguments.
Repeat for Credit
N
Requisites:
Prerequisite: STAT 511 or STAT 771, and prior knowledge of linear algebra and matrix theory e.g., MATH 551, and some programming knowledge e.g., STAT 726.
Typically Offered
Spring-Even Years

STAT 761. Discrete Optimization & Scalability for Data Science.

Credits: 3
Topics covered include computational complexity, NP-hardness, data as networks, graph theoretic algorithms, exact, approximation, heuristic and online algorithms, and connections between convex and non-convex optimization problems. The theory and algorithms are applied to data science problems arising in statistical machine learning, statistical clustering, design of experiments, observational studies, sampling, and variable selection. Applications may be motivated using data from social networks, search engines, the stock market and elections.
Repeat for Credit
N
Requisites:
Prerequisites: STAT 705 or STAT 713 and STAT 720 and programming knowledge e.g. STAT 726.
Typically Offered
Spring-Odd Years

STAT 764. Applied Spatio-Temporal Statistics.

Credits: 3
Construction and analysis of spatial, time-series, and spatio-temporal data sets. Topics include data generation using geographic information systems, exploratory data analysis and visualization, and descriptive and dynamic spatio-temporal statistical models. For context, a focus will be on biological or ecological data.
Repeat for Credit
N
Requisites:
Prerequisites: STAT 510 or STAT 770, and STAT 705, or STAT 713, and STAT 726 or equivalent.
Typically Offered
Spring-Even Years

STAT 766. Applied Data Mining/Machine Learning and Predictive Analytics.

Credits: 3
Addresses the complete process of building analytical tools suitable for learning from data, including automatic online data collection, feature extraction, supervised and unsupervised statistical machine learning methods, evaluation, and report writing. Automatic retrieval of various format online data, including JSON, REST, and Streaming API, http(s), html, xml, and databases. Statistical text processing/mining, state of the art supervised and unsupervised data mining methods, case studies and applications to business, government,social and news media data. Methods include regularized linear and logistic regression, classification trees, nearest neighbor methods, support vector machines, naive Bayes, random forests, boosting/bagging/AdaBoost, clustering, latent Dirichlet allocation, network analysis, and topic modeling models.
Repeat for Credit
N
Requisites:
Prerequisites: STAT 705 or STAT 713 or STAT 717, and prior computer programming proficiency on C, C++, Fortran, R, or Python e.g., CIS 209, STAT 726.
Typically Offered
Fall

STAT 768. Applied Bayesian Modeling and Prediction.

Credits: 3
Bayes rule, principles of Bayesian inference, Bayesian perspective on statistical models, posterior distribution computations using simulations, Markov Chain Monte Carlo (MCMC) (including Gibbs sampling, Metropolis-Hastings algorithm, slice sampler, hybrid forms and alternative algorithms), convergence monitoring and diagnosis, hierarchical models, model checking and model selection, and applications in the sciences using computer software such as R and WinBUGS.
Repeat for Credit
N
Requisites:
Prerequisites: STAT 705 or STAT 713, and STAT 510 or STAT 770.
Typically Offered
Spring-Odd Years

STAT 770. Theory of Statistics I.

Credits: 3
Introduction to probability theory, random variables, transformations of random variables, expectations, discrete and continuous distributions, moment generating functions, families of distributions, multivariate random variables, joint and marginal distributions, conditional distributions and independence, sampling distributions of normal populations, order statistics, convergence of random variables, and the central limit theorem.
Repeat for Credit
N
Requisites:
Prerequisite: MATH 222.
Typically Offered
Fall

STAT 771. Theory of Statistics II.

Credits: 3
Introduction to the sufficiency principle, the likelihood principle, point estimation, uniformly minimum-variance unbiased estimators, hypothesis testing, uniformly most powerful tests, interval estimation, and asymptotic evaluations of estimators and tests.
Repeat for Credit
N
Requisites:
Prerequisite: STAT 770.
Typically Offered
Spring

STAT 799. Topics in Statistics.

Credits: 0-3
Topics in Statistics
Note:
Consent of instructor
Repeat for Credit
Y
Typically Offered
Fall, Spring, Summer

Graduate Courses

STAT 810. Seminar in Probability and Statistics.

Credits: 1
Discussion and lectures on topics in probability and statistics; one seminar talk by each student registered for credit.
Repeat for Credit
Y
Requisites:
Prerequisite: Graduate standing and at least two graduate courses in statistics.
Typically Offered
Fall, Spring

STAT 818. Theory of Life-Data Analysis.

Credits: 3
A study of models and inferential procedures important to life-data analysis. Comparison of estimators (MLE, BLUE, etc.). Pivotal quantities. Design and regression models for non-normal distributions. Analysis of censored data.
Repeat for Credit
Y
Requisites:
Prerequisite: STAT 771 and STAT 705 or STAT 713.
Typically Offered
Fall-Even Years

STAT 842. Probability for Statistical Inference.

Credits: 3
Probability spaces and random elements, distributions, generating and characteristic functions, conditional expectation, convergence modes and stochastic orders, continuous mapping theorems, central limit theory and accuracy, laws of large numbers, asymptotic expansions for approximating functions of random variables and distributions.
Repeat for Credit
Y
Requisites:
Prerequisite: STAT 770 and STAT 771, or equivalent; MATH 633 or equivalent, or concurrent enrollment in MATH 633.
Typically Offered
Fall

STAT 843. Statistical Inference.

Credits: 3
Distributions (commonly used univariate and multivariate distributions, including exponential families of distributions and properties), order statistics and distributional properties, (asymptotic) unbiased estimation and the information inequality, likelihood inference for parametric statistical models (including the multi-parameter case, regular and non-regular cases), confidence sets, functional parameters and statistical functional, density estimation and nonparametric function estimation, permutation methods.
Repeat for Credit
N
Requisites:
Prerequisite: STAT 842; MATH 634 or equivalent, or concurrent enrollment in MATH 634.
Typically Offered
Spring

STAT 850. Stochastic Processes.

Credits: 3
Normal processes and covariance stationary processes; Poisson processes; renewal counting processes; Markov chains; Brownian motion; applications to science and engineering.
Repeat for Credit
Y
Requisites:
Prerequisite: STAT 770.
Typically Offered
Spring-Even Years

STAT 860. Linear Models I.

Credits: 3
Subspaces, projections, and generalized inverses; multivariate normal distribution, distribution of quadratic forms; optimal estimation and hypothesis testing procedures for the general linear model; application to regression models, correlation model.
Repeat for Credit
Y
Requisites:
Prerequisite: STAT 713, STAT 771.
Typically Offered
Fall

STAT 861. Linear Models II.

Credits: 3
Continued application of optimal inference procedures for the general linear model to multifactor analysis of variance, experimental design models, analysis of covariance, split-plot models, repeated measures models, mixed models, and variance component models; multiple comparison procedures.
Repeat for Credit
Y
Requisites:
Prerequisite: STAT 860.
Typically Offered
Spring

STAT 870. Analysis of Messy Data.

Credits: 3
Design structures; treatment structures; equal and unequal variances; multiple comparisons; unequal subclass numbers; missing cells; interpretation of interaction; variance components; mixed models; split-plot and repeated measures; analysis of covariance; cross-over designs.
Repeat for Credit
Y
Requisites:
Prerequisite: STAT 720.
Typically Offered
Fall

STAT 880. Time Series Analysis.

Credits: 3
Autocorrelation function; spectral density; autoregressive integrated moving average processes; seasonal time series; transfer function model; intervention analysis; regression model with time series error.
Repeat for Credit
Y
Requisites:
Prerequisite: STAT 713 and STAT 771.
Typically Offered
Fall-Odd Years

STAT 898. Master's Report.

Credits: 2
Master’s Report
Note:
Consent of instructor
Repeat for Credit
Y
Typically Offered
Fall, Spring, Summer

STAT 899. Master's Thesis Research.

Credits: 1-18
Master’s Thesis Research
Note:
Consent of instructor
Repeat for Credit
Y
Typically Offered
Fall, Spring, Summer

STAT 903. Statistical Methods for Spatial Data.

Credits: 3
Theoretical development of modeling and inference techniques for spatially and/or temporally referenced data, including geostatistical data, lattice data and spatial point patterns; methodology for autocorrelation estimation and prediction; nonstationary spatial processes; asymptotics and computational methods for large spatial datasets; computer-aided applications.
Repeat for Credit
Y
Requisites:
Prerequisite: STAT 771, plus one introductory course in statistical computing (e.g. STAT 726 or equivalent background).
Typically Offered
Spring-Odd Years

STAT 904. Resampling Methods.

Credits: 3
Theoretical development of resampling procedures, including parametric and nonparametric bootstrap, jackknife, randomization and permutation methods; expansion theory for bootstrap inference; implementation of resampling procedures via statistical computational tools.
Repeat for Credit
Y
Requisites:
Prerequisite: STAT 713, STAT 771.
Typically Offered
Spring-Even Years

STAT 905. High-Dimensional Data and Statistical Learning.

Credits: 3
Theoretical framework of statistical methods for the analysis of large-scale data with the aim of understanding data mining, supervised and unsupervised statistical learning techniques from a statistical decision theoretic framework. Methods for model selection, dimensionality reduction, multiple testing control, and estimation in high-dimension data. Emphasis on the conceptual underpinning of different tools for analysis of large-scale data and how they relate to each other. Applications in various fields, including the sciences and engineering, using computer software.
Repeat for Credit
N
Requisites:
Prerequisite: (STAT 713 and STAT 771), or (STAT 727 and STAT 760).
Typically Offered
Fall-Even Years

STAT 907. Bayesian Statistical Inference.

Credits: 3
Principles of Bayesian estimation, testing and prediction; Bayes factors and posterior probabilities of hypotheses; hierarchical modeling; Bayesian model selection and assessment; Bayesian computation and asymptotics; nonparametric Bayesian models.
Repeat for Credit
N
Requisites:
Prerequisite: STAT 713 and STAT 771, plus one introductory course in statistical computing (e.g. STAT 725 or STAT 726 or equivalent background).
Typically Offered
Fall-Odd Years

STAT 920. Experimental Design Theory.

Credits: 3
Incomplete block designs; theory of the construction and analysis of experimental designs.
Repeat for Credit
Y
Requisites:
Prerequisite: STAT 720 and STAT 861.
Typically Offered
Spring-Odd Years

STAT 930. Theory of Multivariate Analysis.

Credits: 3
The multivariate normal distribution, the Wishart distribution, Jacobians of vector and matrix transformations, Hotelling’s T2statistic, the union-intersection principle, tests on mean vectors and covariance matrices, Box’s approximations to critical points, the multivariate general linear model, discriminant analysis, and principal component analysis.
Repeat for Credit
Y
Requisites:
Prerequisite: STAT 730 and STAT 861.
Typically Offered
Spring-Even Years

STAT 940. Advanced Statistical Methods.

Credits: 3
Generalized linear models and generalized mixed models. Statistical models based on the exponential family of distributions. Applications to non-normal and discrete data, including binary, Poisson and gamma regression, and log-linear models. Topics include likelihood-based estimation and testing, model-fitting, residual analyses, over-dispersed models, quasi-likelihood, large sample properties, and the use of computer packages. Also, methods for longitudinal repeated measures data that will include inference for continuous and discrete data. Inferential objectives include prediction of response and estimation of correlation/covariance structures. Nonparametric and semiparametric methods covered as time permits.
Repeat for Credit
Y
Requisites:
Prerequisite: STAT 861, plus one introductory course in statistical computing (e.g. STAT 725 or STAT 726 or equivalent background).
Typically Offered
Fall-Even Years

STAT 941. Advanced Statistical Inference.

Credits: 3
Foundations and methods of modern statistical inference including asymptotic theory in parametric models (including local asymptotic normality and contiguity), efficiency of estimators and tests, Bayes procedures, rank, sign and permutation statistics, U-,M-, L-, R-estimates, chi-square tests, empirical processes and the functional delta method, quantiles and order statistics, inference for nonparametric and semi-parametric models.
Repeat for Credit
Y
Requisites:
Prerequisite: STAT 843.
Typically Offered
Spring-Even Years

STAT 945. Problems in Statistical Consulting.

Credits: 1-3
Principles and practices of statistical consulting. Supervised experience in consultation and consequent research concerning applied statistics and probability associated with on-campus investigations.
Repeat for Credit
Y
Requisites:
Prerequisite: STAT 720 and instructor consent
Typically Offered
On sufficient demand

STAT 950. Advanced Studies in Probability and Statistics.

Credits: 1-3
Theoretical studies of advanced topics in probability, decision theory, Markov processes, experimental design, stochastic processes, or advanced topics.
Note:
Instructor consent
Repeat for Credit
Y
Typically Offered
Fall, Spring, Summer

STAT 999. Research in Statistics.

Credits: 1-18
Research in Statistics
Note:
Consent of instructor
Repeat for Credit
Y
Typically Offered
Fall, Spring, Summer

(Note: STAT 825 has been replaced with STAT 727.)