Master's in Applied Statistics

MATH 421. Statistical Theory I. 3 Credits.

Discrete and continuous random variables, expectation, moments, moment generating functions, properties of special distributions, introduction to hypothesis testing, sampling distributions, Central Limit Theorem, curve of regression, correlation, empirical regression by least squares, maximum likelihood estimation, Neyman-Pearson lemma, likelihood ratio test, power function, chi-square tests, change of variable, "t" and "F" tests, one and two-way ANOVA, nonparametric methods. Prerequisite: MATH 265. F.

STAT 500. Computing for Statistics. 1 Credit.

Use and programming of computer packages for statistics. Preparation for use of software in graduate-level statistics courses. Packages covered may include R, Python, SAS, and others. Prerequisites: At least one course in statistics, and prior programming coursework or experience. Prerequisite: At least one course in statistics and computer programming coursework or experience. F,SS.

STAT 541. Linear Statistical Models. 3 Credits.

Distributions of quadratic forms, general linear hypotheses of full rank, least squares, Gauss-Markoff theorem, estimability, parametric transformations, Cochran's theorem, projection operators and conditional inverses in generalized least squares, applications to ANOVA and experimental design models. Prerequisite: MATH 422 or consent of instructor. F.

STAT 543. Design of Experiments. 3 Credits.

Design and analysis of experimental data. Includes the use of factorial designs, Latin square designs, randomized block designs, split-plot designs and others. Prerequisite: STAT 541. S, odd years.

STAT 545. Multivariate Statistics. 3 Credits.

Theory-based statistical methods for analyzing and displaying multivariate data with applications in machine learning and data mining. Topics include inference in multivariate populations, multivariate analysis of variance, summarizing high dimensional data using principal component analysis, factor analysis, canonical correlation analysis, linear and quadratic methods of classification, cluster analysis, classification trees and random forests, multi-dimensional scaling, and support vector machines. Prerequisites: STAT 500, STAT 541, and MATH 442 or experience with linear algebra concepts. S, even years.

STAT 551. Statistical Graphics. 3 Credits.

Statistical graphics and visualization of one-, two-, or higher-dimensional data. Well-designed graphs and charts are essential for exploration of data, assessment of models, and presentation of results. Includes specific methods as well as general principles, such as effective use of color and motion. Prerequisite: STAT 500. F, even years.