Graduate Statistics Courses
ST 4111/6111. Seminar in Statistical Packages. (1)
One hour lecture. Introduction to the statistical computer packages available at MSU.
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 fi t tests, statistical methods based on rank order, and measures of association.
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 Data Analysis II. (3)
(Prerequisites: MA 4243/6243 and MA 3113). Three hours lecture. Multiple linear regression; fi xed, mixed and random effect models; block designs; two-factor analysis of variance; three-factor analysis of variance; analysis of covariance. Use of SAS. (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 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 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 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 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 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 8263. Advanced Regression Analysis. (3)
(Prerequisite: ST 8253). Three hours lecture. Continuation of ST 8253, including variable selection methods, optimization techniques, biased estimation methods such as ridge regression, non-linear regression, model validation methodology, indicator variables, design models.
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, stratifi ed random, cluster, and systematic sampling; ratio and regression estimation.
ST 8353. Statistical Computations. (3)
(Prerequisite: ST 8114). Three hours lecture. Applications of computer packages, including data screening, t-tests and Hotelling's T", analysis of designed experiments, regression analysis, contingency table analysis, 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 T"; cluster analysis; multidimensional scaling; multivariate analysis of variance.
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 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 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. Recent Developments in Statistics. (3)
(Prerequisite: Consent of instructor). New results in statistical theory and/or statistical methodology; advanced work organized around topics not usually considered in the other courses.
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.