University of Minnesota, Morris : SEAMS Projects

Math 1150 : Introduction to Statistics
Scope, nature, tools, language, and interpretation of elementary statistics. Descriptive statistics; graphical and numerical representation of information; measures of location, dispersion, position, and dependence; exploratory data analysis. Elementary probability theory, discrete and continuous probability models. Inferential statistics, point and interval estimation, tests of statistical hypotheses. Inferences involving one and two populations, ANOVA, regression analysis, and chi-square tests; use of statistical computer packages.

Math 3370 : Combinatorial Mathematics
For students in mathematics, computer science, natural sciences, and related areas in social sciences. Selected topics for permutations and combinations, generating functions, recurrence relations, 0-1 matrices, partitions, inclusion and exclusion, graphs, trees and circuits, bipartite graphs, planar graphs, and networks. Note : this class is offered once every two years.

Math 3605 : Statistical Methods
Descriptive statistics, elementary probability theory; laws of probability, random variables, discrete and continuous probability models, functions of random variables, mathematical expectation. Statistical inference; point estimation, classical and Bayesian methods of estimation, interval estimation, tests of hypotheses. Other statistical methods; linear regression and correlation, ANOVA, nonparametric statistics, statistical quality control, use of statistical computer packages.

Math 3620 : Elementary Statistical Data Analysis
Nature and objectives of statistical data analysis, exploratory and confirmatory data analysis techniques. Some types of statistical procedures; formulation of models, examination of the adequacy of the models. Some special models; simple regression, correlation analysis, multiple regression analysis, analysis of variance, use of statistical computer packages.

Math 3630 : Discrete Statistical Multivariate Analysis
Analysis of categorical data. Loglinear models for two- and higher dimensional contingency tables; model selection, ordered categories, fixed margins and logit models, casual analysis involving logit and loglinear models, fixed and random zeroes, use of statistical computer packages. Note : this course is offered every two years opposite Math 3640)

Math 3640 : Applied Continuous Statistical Multivariate Analysis
Aspects of multivariate analysis, random vectors, sample geometry and random sampling, multivariate normal distribution, inferences about mean vector, MANOVA. Analysis of covariance structures : principal components, factor analysis. Classification and grouping techniques : discrimination and classification, clustering, use of statistical computer packages. Note : this course is offered every two years opposite Math 3630)

Math 3690 : Topics in Statistics
Topics selected from nonparametric methods, linear and nonlinear regression analysis, ANOVA, design of experiments, sampling methods, time series analysis, simulation and statistical computing.