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Course Criteria
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3.00 Credits
In this course, students solve statistical problems on the computer with the help of statistical packages, such as SAS, BMD, Mystat, etc., and learn to interpret the outputs and draw inferences. Topics include analysis of variance with and without interactions, correlation and regression analysis, general linear models, multiple comparisons, and analysis of contingency tables. Prerequisite: MATH 324
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3.00 Credits
This is a comprehensive treatment of regression analysis course, statistical topics including: simple linear regression, least square estimates, ANOVA table, F-test, R-square, multiple regression, using dummy variables, selections of the "best subset" of predictor variable,checking model assumptions, and Logistic regression. The computer package SAS will be used through out the course and applications to real life data will be an integral part of the course. Prerequisite: MATH 324
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3.00 Credits
Treatment of numerical methods including numerical integration, numerical solution of equations and systems of equations, approximation of functions, numerical solution of differential equations, applications, and computer implementation of numerical methods. Prerequisite: MATH 202 or MATH 322
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3.00 Credits
Iterative Algorithms, Optimization process and Linear Programming (LP), including the Graphical method and Simplex method. Duality and Sensitivity analysis, LP applications in business and health. Nonlinear Unconstrained problems and various Descent methods. Nonlinear Constrained optimization, including Primal, Penalty, and Barrier methods. Prerequisite: MATH 202
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3.00 Credits
A topic not covered by an existing course is offered as recommended by the department and approved by the dean. The number of credits for MATH 399 may vary from 1 to 3 for a selected topic. MATH 399 cannot be credited more than twice, each on a different topic, toward degree requirements. Prerequisite: Department Chairperson's permission 1-3 credits Science and Healt294 h
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3.00 Credits
Concepts of modern algebra are applied to different areas. Topics include Boolean algebra and applications to switching theory; automata (finite state machines) and Turing machines; recursive functions and some ideas in theory of computability, groups, rings, polynomial rings, finite fields applied to coding theory, development of binary group codes, Hamming codes, B-C-H codes, relations of geometry, and statistical block designs to codes; importance of codes in communications. Prerequisite: MATH 301 or MATH 202
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3.00 Credits
This is an advanced course in discrete mathematics, primarily dealing with discrete dynamical systems, algorithms, combinatorics, and graph theory. Emphasis is placed on complexity of algorithms, on existence and optimization problems in graph theory and on associated algorithms. Prerequisite: MATH 202 or CS 260
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3.00 Credits
An applied statistical methods course in time series modeling of empirical data observed over time. Topics covered include: Linear Time Series Models, Stationary Processes, Moving Average Models, Autoregressive Models, ARIMA Models, Estimation using Time Series Models, Data Analysis with Time Series Models, Forecasting, and Forecast Errors and Confidence Intervals. Prerequisite: MATH 334
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3.00 Credits
For processes of any kind that have measurable inputs and outputs, Design of Experiments (DOE) methods guide you in the optimum selection of inputs for experiments, and in the analysis of results. Full factorial as well as fractional factorial designs are covered. Software such as SAS or S-Plus will be used for testing and regression problems. Prerequisite: MATH 324
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3.00 Credits
Topics are selected from various branches of applied mathematics. The mathematical principles and theories involved are applied to problems in the physical sciences, mathematics, biological sciences, business, and computer science. Prerequisite: MATH 322
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