Applied Statistics (MS)

The topology of Euclidean spaces, norms, classical inequalities, local and global properties of continuous functions, preservation of compactness and connectedness, sequences in Euclidean space and sequences of functions.

Prerequisites: none

This course presents topics from mathematical analysis of both discrete and continuous models taken from problems in the natural sciences, economics, and resource management.

Prerequisites: none

An in-depth study of linear operators and their related spaces, dimension, rank, matrix representation of linear operators, special matrices, determinants, eigenvectors, and eigenvalues.

Prerequisites: none

This course provides an introduction to techniques and analysis involved with solving mathematical problems using technology. Topics included are errors in computation, solutions of linear and nonlinear equations, numerical differentiation and integration, and interpolation.

Prerequisites: none

Randomized complete block design, Latin squares design, Graco- Latin squares design, balanced incomplete block design, factorial design, fractional factorial design, response surface method, fixed effects and random effects models, nested and split plot design.

Prerequisites: none

Topics include: sampling distributions, means and variances; bias, robustness and efficiency; random sampling; systematic sampling methods including stratified random, cluster and two-state sampling; and ratio, regression, and population size estimation. Suitable software, such as MATLAB, R, SAS, etc., is introduced.

Prerequisites: none

Topics on multivariate analysis for discrete data, including two/higher dimensional tables; models of independence; log linear models; estimation of expected values; model selection; and logistic models, incompleteness and regression. Suitable statistical software, such as MATLAB, R, SAS, etc., is introduced.

Prerequisites: none

Topics include derivation and usage of nonparametric methods in univariate, bivariate, and multivariate data; applications in count, score, and rank data; analysis of variance for ranked data; and regression estimation. Suitable software, such as MATLAB, R, SAS, etc., is introduced.

Prerequisites: none

Applications of discrete and continuous mathematics to deterministic problems in the natural sciences, computer science, engineering, and economics. Applied problems will be developed within the mathematical framework of dimensional analysis, asymptotic analysis, perturbation theory, stability, and bifurcation.

Prerequisites: none

Can be used for any graduate level applied mathematics course not offered as a regular course. Distinct offerings may be repeated for credit.

Prerequisites: none

Optimal conditions for constrained and unconstrained optimization problems, and a comprehensive description of the most powerful, state-of-the-art, techniques for solving continuous optimization problems. Large-scale optimization techniques are emphasized in the course.

Prerequisites: MATH 517 and MATH 547

This course is an in-depth study of solving ordinary differential equations and partial differential equations numerically. Runge-Kutta methods and general multi-step methods are developed for ordinary differential equations. Finite Difference Method and Finite Element methods are developed for partial differential equations. Error control and step size changing for both stiff and non-stiff equations are analyzed.

Prerequisites: none

This course is an in-depth study of solving algebraic eigenvalue problems, least-square problems, direct and iterative methods for solving linear systems, and their applications.

Prerequisites: none

Bayesian Statistics is an alternative to Frequentist statistics. Bayesian inference uses probability for both hypotheses and data. In Bayesian statistics, population parameters are considered random variables having probability distributions. The probabilities measure a degree of belief in the parameters. Bayes¿ theorem is used to reformulate the beliefs using observed data. This course introduces the Bayesian approach to statistical inference and describes effective approaches to Bayesian modeling and computation.

Prerequisites: MATH/STAT 555 and MATH/STAT 556 with a grade of "C" (2.0) or better or consent.

Most statistical analysis and modeling techniques involve assumptions about the independence of the data. However, many real life data occur in the form of time series where observations are dependent. In this course, we will concentrate on both univariate and multivariate time series analysis and model building strategies with time dependent data. Available software will be used to complete the data analysis projects with a balance between theory and applications.

Prerequisites: none

Matrix theory, multivariate normal distribution of quadratic forms, estimation and hypothesis testing in the general linear model, and applications of linear models.

Prerequisites: none

Statistical tools used to analyze data in biological and medical research. Topics covered are Statistical Theory, Concepts of Statistical Inference, Regression and Correlation Methods, Analysis of Variance, Survival Analysis and Study Designs. Applications to medical problems.

Prerequisites: none

This course will cover the basic concepts of big data with an emphasis on the statistical techniques for analyzing structured and unstructured data. Students will learn concepts, techniques and tools that are necessary for working with the various facets of data science practice, including data collection and integration, exploratory data analysis, predictive modeling, descriptive modeling, data product creation, evaluation, and effective communication. The course has applications across many disciplines such as engineering, computer science, statistics, mathematics, economics and management. Prerequisite: MATH 247 and STAT 354 or instructor consent

Prerequisites: none

A graduate course in a particular area of statistics not regularly offered. May be repeated for credit on each new topic.

Prerequisites: none

Statistical package programs used in data collection, transformation, organization, summarization, interpretation and reporting, statistical description and hypothesis testing with statistical inference, interpreting outputs, chi-square, correlation, regression, analysis of variance, nonparametrics, and other designs, accessing and using large files (U.S. Census data, National Health Survey, etc.) Same as COMS 696

Prerequisites: none