ST 2113. Introduction to Statistics. (3)
(Prerequisite: ACT Math sub-score 24(or higher for some sections) or grade of C or better in MA1103 or MA1313 or MA1213.). Two hours lecture. Two hours laboratory. Introduction to statistical techniques: descriptive statistics, random variables, probability distributions, estimation, confidence intervals, hypothesis testing, and measurement of association. Computer instruction for statistical analysis. (Same as MA 2113). For the Mathematics Placement Exam, go to: https://www.math.msstate.edu/undergrad/placement.
ST 2990. Special Topics in Statistics. (1-9)
Credit and title to be arranged. This course is to be used on a limited basis to offer developing subject matter areas not covered in existing courses. (Courses limited to two offerings under one title within two academic years).
ST 3123. Introduction to Statistical Inference. (3)
(Prerequisite: ACT Math subscore of 24, or grade of C or better in MA 1313). Two hours lecture. Two hours laboratory. Basic concepts and methods of statistics, including descriptive statistics, probability, random variables, sampling distributions, estimation, hypothesis testing, introduction to analysis of variance, simple linear regression. (Same as MA 3123).
ST 3133. Statistics I. (3)
(Prerequisite: MA 1713). Three hours lecture. Focus on descriptive statistics. Foundational concepts and methods of descriptive statistics for quantitative and categorical data: data types and structures; tables and graphical summaries; measures of center, spread, and position; shape (skewness, kurtosis); exploratory data analysis; bivariate summaries (correlation) and an introduction to simple linear regression. Emphasis on interpretation and communication.
ST 3213. Statistics II. (3)
(Prerequisite: MA 1723). Three hours lecture. Focus on probability. An introductory, application-oriented course in probability for statistics majors: sample spaces and events; probability rules; counting; conditional probability and Bayes' rule; discrete and continuous random variables; common distributions (Bernoulli, Binomial, Geometric, Hypergeometric, Poisson, Uniform, Exponential, Normal); expectation and variance; basics of joint distributions and covariance; ideas of sampling distributions, the Law of Large Numbers, and an intuitive Central Limit Theorem. Emphasis on problem solving, real examples, and simulation.
ST 3223. Statistics III. (3)
(Prerequisites: ST 3133 and ST 3213). Three hours lecture. Focus on introduction to inference. An applied introduction to statistical inference for real data: sampling distributions and variability, confidence intervals, hypothesis testing, comparing groups (means and proportions), categorical data analysis (chi-square), one-way ANOVA, and an introduction to simple linear regression inference and resampling (bootstrap/permutation). Emphasis on assumptions, interpretation, effect sizes, and clear communication using statistical software.
ST 3333. Introduction to Statistics Computation. (3)
(Prerequisite: ST 3133). Three hours lecture. An introduction to statistical computing with the R language: R/RStudio workflow; objects, vectors, and data frames; importing, cleaning, and reshaping data; visualization with the ggplot2 package; data wrangling with the tidyverse package; control flow, vectorization, and functions; iteration (the purrr map family); reproducible reports with R Markdown/Quarto; simulation (random number generation, Monte Carlo); basic modeling (simple regression, multiple regression overview); and good coding practices, documentation, and ethics.
ST 4000. Directed Individual Study in Statistics. (1-6)
Hours and credits to be arranged.
ST 4111/6111. Seminar in Statistical Packages. (1)
One hour lecture. Introduction to the statistical computer packages available at MSU.
ST 4113. Regression. (3)
(Prerequisites: ST 3223 and ST 3333). Three hours lecture. An introductory course to linear regression theory and applications: simple and multiple linear regression, estimation and hypothesis testing, model selection, multicollinearity, qualitative predictors, interactions, and residual diagnostics. Emphasizing model fitting, inference, diagnostics, and interpretation using R.
ST 4123. Design of Experiments. (3)
(Prerequisites: ST 3223 and MA 3113). Three hours lecture. This course bridges the classical theory of experimental design, developed by R. A. Fisher, J. S-Neyman, and others, with the modern revolution in causal inference. We will begin with the fundamental principles of randomization, blocking, and factorial designs, and then progress to contemporary challenges and designs that are prevalent in fields such as technology (A/B testing), medicine, public policy, and the social sciences.
ST 4143. Bayesian Statistics. (3)
(Prerequisite: ST 4113). Three hours lecture. This course introduces the Bayesian approach to statistical inference, emphasizing both theory and computation. Beginning with simple models based on normal and binomial distributions, the course illustrates concepts of conjugate and non-informative priors for single- and multi-parameter models. Topics include Bayes’ theorem, prior and posterior distributions, hypothesis testing, Bayesian regression, and model comparison using Bayes factors. Computational methods such as Monte Carlo simulation and Gibbs sampling algorithms are presented with an emphasis on implementation in R and BUGS.
ST 4211/6211. Statistical Consulting. (1)
(Prerequisite: Consent of the department). (May be repeated for credit.) Provides students with the opportunity to participate as statistical consultants on real projects; consultants are required to attend a weekly staff meeting.
ST 4213/6213. Nonparametric Methods. (3)
(Prerequisite: An introductory course in statistical methods). Three hours lecture. Nonparametric and distribution-free methods, including inferences for proportions, contingency table analysis, goodness of fit tests, statistical methods based on rank order, and measures of association.
ST 4223/6223. Gambling and Gaming. (3)
(Prerequisite: any introductory statistics course) Three hours lecture. This course investigates technical aspects of gambling and gaming. The theoretical underpinnings of all games of chance lie in probability theory. The rules of several games of chance will be examined, then statistical quantification of risk and reward are developed.
ST 4243/6243. Data Analysis I. (3)
(Prerequisite: MA 2743. Co-requisite: MA 3113). Three hours lecture. Data description and descriptive statistics, probability and probability distributions, parametric one-sample and two-sample inference procedures, simple linear regressions, one-way ANOVA. Use of SAS. (Same as MA 4243/6243.)
ST 4253/6253. Statistical Learning. (3)
(Prerequisite: MA/ST 4243/6243). Three hours lecture. Concepts and implementation of selected statistical and machine learning methods; predictive modeling; regression and classification problems; data handling for both quantitative and categorical types; model evaluation/selection; and relevant interpretation. (Same as MA 4253/6253).
ST 4313/6313. Introduction to Spatial Statistics. (3)
(Prerequisite: Grade of C or better in ST 3123, or equivalent). Two hours lecture. Two hours laboratory. Spatial data analysis; kriging, block kriging, cokriging, variogram models; median polish and universal kriging for mean-nonstationary data; spatial autoregressive models; estimation and testing; spatial sampling.
ST 4333/6333. Statistics in Finance. (3)
(Pre-requisite: MA 2733, ST/MA 3123). Three hours lecture. This course aims to give an account of the main uses of probability and statistics in finance. It will cover mathematical and statistical aspects of interest and insurance, mean (expected return) and variance aspects of portfolios with multiple assets, efficient frontier and optimal portfolios. (Same as MA 4333/6333).
ST 4413. Multivariate Statistics. (3)
(Prerequisite: ST 4113). Three hours lecture. This course introduces the fundamental concepts and methods of multivariate statistical analysis—an essential collection of techniques for studying data sets with multiple, often correlated variables. The focus is on the practical application of these methods using the statistical computing language R, while also covering the motivating theory and underlying assumptions. Topics include the multivariate normal distribution, hypothesis testing, multivariate regression, multivariate analysis of variance (MANOVA), methods for dimension reduction, clustering, and classification.
ST 4523/6523. Introduction to Probability. (3)
(Prerequisite: MA 2733). Three hours lecture. Basic concepts of probability, conditional probability, independence, random variables, discrete and continuous probability distributions, moment generating function, moments, special distributions, central limit theorem. (Same as MA 4523/6523).
ST 4543/6543. Introduction to Mathematical Statistics I. (3)
(Prerequisite: MA 2743). Three hours lecture. Combinatorics; probability, random variables, discrete and continuous distributions, generating functions, moments, special distributions, multivariate distributions, independence, distributions of functions of random variables. (Same as MA 4543/6543).
ST 4553. Introduction to Stochastic Modeling. (3)
(Prerequisite: MA/ST 4523 or MA/ST 4543/6543) Three hours lecture. Introductory treatment of probability models and stochastic processes such as finite-state Markov chains, Poisson Processes, dynamic programming, hazard analysis, decision analysis, and simulation. This course considers both theory and application.
ST 4573/6573. Introduction to Mathematical Statistics II. (3)
(Prerequisite: ST 4543/6543). Three hours lecture. Continuation of ST 4543/6543. Transformations, sampling distributions, limiting distributions, point estimation, interval estimation, hypothesis testing, likelihood ratio tests, analysis of variance, regression, chi-square tests. (Same as MA 4573/6573).
ST 4783. Statistical Quality Control. (3)
(Prerequisite: ST 3223 or equivalent). Three hours lecture. Concepts, methods, and applications of quality control and improvement using statistical techniques. Topics include the theory of control charts for variables and attributes, Construction and Operation of X.bar- and R.bar-Charts, process capability analysis, acceptance sampling, and introduction to design of experiments for quality improvement.
ST 4990. Special Topics in Statistics. (1-9)
Credit and title to be arranged. This course is to be used on a limited basis to offer developing subject matter areas not covered in existing courses. (Courses limited to two offerings under one title within two academic years).
ST 6990. Special Topics in Statistics. (1-9)
Credit and title to be arranged. This course is to be used on a limited basis to offer developing subject matter areas not covered in existing courses. (Courses limited to two offerings under one title within two academic years).
ST 7000. Directed Individual Study in Statistics. (1-6)
Hours and credits to be arranged.
ST 8114. Statistical Methods. (4)
(Prerequisite: MA 1313). Three hours lecture. Two hours laboratory. Fall and Spring semesters. Descriptive statistics; sampling distributions; inferences for one and two populations; completely random, block, Latin square, split-plot designs; factorials; simple linear regression; chi-square tests.
ST 8123. Statistical Thinking: Probability Models and Theory of Statistics. (3)
(Prerequisite: MA 2733). Three hours lecture. This course introduces concepts and theory of statistical inference, focuses on how to use data to infer (estimation and testing) about the unknown parameters and to do so in the most optimal way, it also covers basic theory of Bayesian inference.
ST 8133. Statistical Modeling. (3)
(Prerequisite: ST 8123). Three hours lecture. This course introduces statistical modeling in wide variety of situations, modeling univariate data with an appropriate probability distribution, modeling of bivariate and multivariate data using general linear modeling (regression and design models), modeling binary data through logit link function, modelling categorical data.
ST 8214. Design and Analysis of Experiments. (4)
(Prerequisite: ST 8114) Three hours lecture. Three hours laboratory. Offered spring semester. Procedures in planning and analyzing experiments; simple, multiple, and curvilinear regression; factorial arrangement of treatments; confounding; fractional replication; block designs; lattices; split-plots.
ST 8223. Statistical Models for Option Pricing. (3)
(Prerequisite: ST/MA 4543/6543) Three hours lecture. This course deals with mathematical and statistical aspects of the financial derivative called option pricing. Focus will be on the binomial option price model, time series models and geometric Brownian motion as a limiting binomial model.
ST 8253. Regression Analysis. (3)
(Prerequisite: ST 8114 or equivalent). Three hours lecture. Fall and Spring semesters. Simple linear regression analysis and related inferences, remedial measures, multiple and polynomial regression, use of indicator variables, variable selection methods, and use of computer.
ST 8273. Advanced Regression Analysis. (3)
(Prerequisite: ST 8253). Three hours lecture. Continuation of ST 8253, including non-linear regression models for continuous response variables, generalized linear models such as logistic regression models for binary data, and log-linear regression models for count data.
ST 8313. Introduction to Survey Sampling. (3)
(Prerequisite: ST 8114). Three hours lecture. Topics include: design, planning, execution, and analysis of sample surveys; simple random, stratified random, cluster, and systematic sampling; ratio and regression estimation.
ST 8353. Statistical Computations. (3)
(Prerequisite: ST 8114). Three hours lecture. Programming with R, including an introduction to the R language, programming statistical graphics, numerical optimization, simulation study, parallel computation, high accuracy computation, projects, and report writing.
ST 8413. Multivariate Statistical Methods. (3)
(Prerequisite: ST 8253). Three hours lecture. Multivariate normal; multiple and partial correlation; principal components; factor analysis; rotation; canonical correlation; discriminant analysis; Hotelling's T2; cluster analysis; multidimensional scaling; multivariate analysis of variance.
ST 8433. Multivariate Statistical Analysis. (3)
(Prerequisites: ST 8413 and ST 8613 or consent of instructor). Three hours lecture. Theory of multivariate statistical methodology, including multivariate normal and Wishart distributions, Hotelling’s T2, classification, multivariate analysis of variance and covariance, canonical correlation, principal components analysis.
ST 8533. Applied Probability. (3)
(Prerequisite: ST 4543/6543). Three hours lecture. An introduction to the applications of probability theory. Topics include Markov Chains, Poisson Processes, and Renewal, Queueing, and Reliability theories. Other topics as time permits.
ST 8553. Advanced Probability Theory. (3)
(Prerequisites: ST 6543 and MA 8633 or consent of instructor). Three hours lectures. A measure-theoretic presentation of the theory of probability including independence and conditioning, convergence theorems, characteristics functions, and limit theorems.
ST 8563. Advanced Stochastic Processes. (3)
(Prerequisite: ST 8553 or consent of instructor). Three hours lecture. Continuation of ST 8553, including Markov processes, second-order processes, stationary Processes, Ergodic theory, martingales, stopping times, and Brownian motion.
ST 8603. Applied Statistics. (3)
(Prerequisite: ST 4253/6253 or equivalent). Three hours lecture. Advanced analysis of experimental data. Topics include mixed and random models, incomplete block design, changeover trials, experiments, analysis of covariance, and repeated measures design.
ST 8613. Linear Models I. (3)
(Prerequisites: ST 4253/6253 and 4573/6573). Three hours lecture. Random vectors, multivariate normal, distribution of quadratic forms, estimation and statistical inferences relative to the general linear model of full rank, theory of hypothesis testing.
ST 8633. Linear Models II. (3)
(Prerequisite: ST 8613). Three hours lecture. Continuation of ST 8613, including generalized inverses; general linear model not of full rank, related inferences, applications; computing techniques; design models, analyses, hypothesis testing; variance-component models.
ST 8733. Advanced Statistical Inference I (3)
(Prerequisites: MA/ST 4573/6573 or consent of instructor). Three hours lecture. Theoretical statistics, including sufficiency and completeness, UMVU estimators, likelihood estimation, Bayesian estimation, UMP tests, likelihood-based tests, sequential tests, optimality, and asymptotic properties.
ST 8743 Advanced Statistical Inference II. (3)
(Prerequisites: ST 8733 or consent of instructor). Three hours lecture. Theoretical statistics, including order statistics, power functions, efficiency, asymptotic theory, nonparametric rank-based hypothesis testing, permutation testing, M estimation, jackknife procedure, and bootstrap procedure.
ST 8853. Advanced Design of Experiments I. (3)
(Prerequisite: ST 8603 or ST 8214). Three hours lecture. Noise reducing designs; incomplete block designs; factorial experiments, Yates' algorithms, confounding systems; fractional replication; pooling of experiments; nested designs; repeated measurement designs; messy data analyses.
ST 8863. Advanced Design of Experiments II. (3)
(Prerequisites: ST 8853 and ST 8613). Three hours lecture. Continuation of ST 8853, including analysis of covariance, splitplot designs and variants, applications of the general linear model, response surface methodology, randomization models, pseudo-factors, and cross-over design.
ST 8913. Smoothing Methods in Statistics. (3)
(Prerequisite: ST 6573 or ST 8123). Three hours lecture. Basic ideas of nonparametric estimation, Kernel-based smoothing methods of univariate density and regression estimation, mathematical analysis of kernel smoothing, bias reduction, optimal and data-based bandwidth choices, estimations of functions related to density and regression functions.
ST 8923. Time Series. (3)
(Prerequisite ST 8123). Three hours lecture. Survey of modeling and forecasting methods for random processes that evolve over time.
ST 8951. Seminar in Statistics. (1)
(Prerequisite: Consent of instructor). (May be repeated for credit). Review of literature on assigned topics; discussions and presentations of papers.
ST 8990 Special Topics in Statistics. (1-9)
Credit and title to be arranged. This course is to be used on a limited basis to offer developing subject matter areas not covered in existing courses. (Courses limited to two offerings under one title within two academic years).
ST 9000 Dissertation Research /Dissertation in Statistics. (1-13)
Hours and credits to be arranged.