Applied Statistics
Graduate Programs
Description
The Department of Mathematics and Statistics offers four programs leading to a master’s degree in Mathematics and Statistics. The Master of Arts programs allow students to study pure mathematics, mathematics for community college teaching, or mathematics and information technology. The Master of Science programs allow students to specialize in secondary teaching, or mathematics and statistics.
Nineteen graduate faculty members support the graduate programs. Faculty specialties include algebraic combinatorics, applied computational mathematics, combinatorial matrix theory, complex analysis, discrete mathematics, dynamical systems, elementary and secondary mathematics education, differential geometry, industrial mathematics, mathematical biology, numerical analysis, optimization, partial differential equations, probability, real analysis, ring theory, statistics, stochastic differential equations, stochastic processes, and topology.
Graduates of the program have found employment in a variety of fields, including software engineering, graphic design, insurance, community college teaching, secondary mathematics teaching and as statisticians. Others have gone on to obtain a Ph.D. in mathematics or statistics.
Facilities. The Department of Mathematics and Statistics is housed in Wissink Hall. This building is also home to the Academic Computing Center which supports over 400 up-to-date workstations on both PC and Macintosh platforms, and a computer-equipped classroom designed for the teaching of mathematics and statistics. The classroom computers are equipped with mathematical and statistical software including Mathematica, Matlab, Maple, SAS, SPSS, Geometer’s Sketchpad, R, Python, and Minitab. Students also have access to Unix mainframe machines and a High Performance Computing (HPC) cluster. The internet is easy to access through the campus-wide wireless network. The library has a wide range of mathematical texts and journals. The library also supports services which provide access to literature not found in our library’s collection.
Majors |
Program | Locations | Total Credits |
|---|---|---|---|
| Applied Statistics MS | MS - Master of Science |
|
34 |
Policies & Faculty
Policies
Admission. Preference will be given to applicants with minimum grade point average of 3.0 and a demonstrated ability to consistently perform at a B or better level in upper division mathematics and/or statistics courses. An applicant must also meet the general admission requirements of the College of Graduate Studies and Research.
Financial Assistance. Approximately 30 graduate assistantships are available in the department each year. Graduate assistant duties include teaching or research.
General Program Requirements. All programs require an alternate plan paper or thesis, a comprehensive exam, and an oral defense of the alternate plan paper or thesis. At least 50% of the course work of each program must be at the 600 level. Alternate plan paper and thesis credit are not counted as course work. After completing 16 credits, the student must select an examining committee composed of a minimum of three graduate faculty members.
Course Application Policy. No course can be used to satisfy more than one program requirement.
Contact Information
College of Science, Engineering, & Technology
Department of Mathematics and Statistics273
Wissink Hall
507-389-1453
http://cset.mnsu.edu/mathstat/
500 Level
Credits: 4
An introduction to topological spaces and their fundamental properties such as compactness, connectedness, separation properties and countability properties. Continuous functions between topological spaces and common examples of topological spaces are also discussed.Prerequisites: MATH 290
Credits: 4
Algebra and geometry of complex numbers, analytic functions, power series, Cauchy's theorem and residue theorem.Prerequisites: none
Credits: 4
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
Credits: 3
A continuation of MATH 4/517. The course may include topics from metric spaces, Riemann-Stieltjes integration, differentiation in Euclidean space, sequences and series of functions, approximation theorems, implicit and inverse function theorems, equicontinuity, and mapping theorems.Prerequisites: none
Credits: 4
This course presents the theory, computations, and applications of partial differential equations and Fourier series.Prerequisites: none
Credits: 4
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
Credits: 4
Simplex method and its variants, duality, sensitivity analysis, interior-point methods, quadratic programming and linear complementarity problems. Applications such as classification problems and game theory with linear optimization software.Prerequisites: MATH 122 and MATH 247
Credits: 4
Geometry of spaces including Euclidean and non-Euclidean and applications of contemporary geometry.Prerequisites: MATH 257 and MATH 290 with a grade of "C" (2.0) or higher, or consent.
Credits: 4
Euclidean algorithm, primes, composites, number theoretic functions, congruences, Diophantine equations, Euler and Fermat theorems, and algebraic number fields.Prerequisites: none
Credits: 4
A continuation of MATH 345. The course will include topics from groups, rings, and fields.Prerequisites: none
Credits: 3
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
Credits: 3
Simple and multiple regression, correlation, analysis of variance and covariance.Prerequisites: none
Credits: 3
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
Credits: 4
A mathematical approach to statistics with derivation of theoretical results and of basic techniques used in applications. Includes probability, continuous probability distributions, multivariate distributions, functions of random variables, central limit theorem, and statistical inference. Same as MATH 555.Prerequisites: none
Credits: 4
A mathematical approach to statistics with derivation of theoretical results and of basic techniques used in applications. Includes probability, continuous probability distributions, multivariate distributions, functions of random variables, central limit theorem, and statistical inference. Same as STAT 555Prerequisites: none
Credits: 4
A mathematical approach to statistics with derivation of theoretical results and of basic techniques used in applications, including sufficient statistics, additional statistical inference, theory of statistical tests, inferences about normal models, and nonparametric methods. Same as MATH 556.Prerequisites: none
Credits: 4
A mathematical approach to statistics with derivation of theoretical results and of basic techniques used in applications, including sufficient statistics, additional statistical inference, theory of statistical tests, inferences about normal models, and non-parametric methods. Same as STAT 556.Prerequisites: none
Credits: 3
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
Credits: 3
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
Credits: 3
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
Credits: 3
This course applies probabilistic methods to problems encountered in actuarial science that prepares students for the Society of Actuaries Exam P/1.Prerequisites: Math/Stat 354 and Math 223 or Math/Stat 455 or Math/Stat 555.
Credits: 4
This course covers the theory of interest portion of Exam FM/2 of the Society of Actuaries. Topics include time value of money, measurement of interest, annuities certain, arithmetic and geometric annuities, amortization schedules and sinking fund, bonds and other securities, yield rates, and interest rate immunization.Prerequisites: MATH 223
Credits: 4
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
Credits: 4
This course is a continuation of MATH 470. Topics included are the algebraic eigenvalue problem, least-squares approximation, solutions of systems of nonlinear equations, and numerical solutions of ordinary differential equations.Prerequisites: none
Credits: 4
Students will learn fundamental concepts of computer programming and write software to implement a variety of mathematical algorithms, manipulate large amounts of data, test conjectures, and make abstract mathematical concepts concrete. Programming concepts include input versus output, data structures, local and global variables, switch statements, iteration, recursion, halting conditions, modularity, debugging, and algorithm analysis. Programming projects may vary with instructor, but could include topics from enumerative combinatorics, graph theory, group theory, linear algebra, and number theory.Prerequisites: Math 375 and Math 345 with grade of C (2.0) or better in both courses.
Credits: 3
The development of selected topics from before the Hellenistic time period to the late twentieth century. Familiarity with the content of HIST 180 is beneficial.Prerequisites: none
Credits: 3
.Prerequisites: none
Credits: 3
This course is designed to inform secondary mathematics teachers about effective utilization of technology in the mathematics curriculum.Prerequisites: none
Credits: 1-3
A course of study in which a group of students study a topic by examining results through reports and discussions. May be repeated for credit on each new topic.Prerequisites: none
Credits: 1-3
The study of a particular topic primarily based upon recent literature. May be repeated for credit on each new topic.Prerequisites: none
Credits: 1-4
A short course devoted to a specific mathematical topic. May be repeated for credit on each new topic.Prerequisites: none
Credits: 1-4
A course designed to upgrade the qualifications of persons on-the-job. May be repeated on each new topic.Prerequisites: none
Credits: 1-4
.Prerequisites: none
Credits: 1-4
A course in an area of mathematics not regularly offered. May be repeated on each new topic.Prerequisites: none
Credits: 1-12
Provides a student the opportunity to gain expertise and experience in a special field under the supervision of a qualified person.Prerequisites: none
Credits: 1-12
Provides a student the opportunity to gain expertise and experience in a special field under the supervision of a qualified person.Prerequisites: none
600 Level
Credits: 3
Mathematical concepts of graph theory applied to problems that have algorithmic solutions.Prerequisites: none
Credits: 3
Can be used for any graduate level discrete mathematics course not offered as a regular course. Distinct offerings may be repeated for credit.Prerequisites: none
Credits: 3
Measure theory, integration, metric spaces, and Banach spaces.Prerequisites: none
Credits: 3
Can be used for any graduate level analysis course not offered as a regular course. Distinct offerings may be repeated for credit.Prerequisites: none
Credits: 3
An introduction to the basic concepts and principles of functional analysis. Normed spaces, Banach spaces, Hilbert spaces, and approximation theory are studied.Prerequisites: none
Credits: 3
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
Credits: 3
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
Credits: 3
The theory of functions of one complex variable. Complex numbers, contour integration, analytic functions, residues, and power series.Prerequisites: none
Credits: 3
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
Credits: 3
This course presents selected topics in projective, transformational, and differential geometry.Prerequisites: none
Credits: 3
A rigorous excursion through some of the topics of abstract algebra which are essential components of the background of a masters level graduate student. Abstract topics include groups, rings, fields, and modules. Concrete applications include properties of the integers, polynomial rings, and the symmetric group.Prerequisites: none
Credits: 3
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.
Credits: 3
This course will cover advanced topics such as (but not limited to) free abelian groups, group rings, noetherian/generalized noetherian rings, coherent/generalized coherent rings, homological algebra, homological dimension theory, representation theory of finite fields, galois theory of equations, field theory, valuation theory, and semigroups.Prerequisites: none
Credits: 3
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
Credits: 3
Matrix theory, multivariate normal distribution of quadratic forms, estimation and hypothesis testing in the general linear model, and applications of linear models.Prerequisites: none
Credits: 3
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
Credits: 3
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 consentPrerequisites: none
Credits: 3
Heuristics in mathematical problem solving and mathematical modeling for teachers.Prerequisites: none
Credits: 3
Algebraic concepts and procedures interpreted and related from the perspectives of abstract algebra, cognitive research on the learning of algebra, and professional curriculum and instruction programs.Prerequisites: none
Credits: 3
The Van Hiele model of the development of geometric thought and recent developments of geometric theory and applications which are related to the school mathematics curriculum.Prerequisites: none
Credits: 3
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
Credits: 3
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
Credits: 1-4
Independent individual study under the guidance and direction of a graduate faculty member.Prerequisites: none
Credits: 1-4
Independent individual study under the guidance and direction of a graduate faculty member.Prerequisites: none
Credits: 1-4
A graduate course in an area of mathematics not regularly offered. May be repeated for credit on each new topic.Prerequisites: none
Credits: 1-4
A graduate course in a particular area of statistics not regularly offered. May be repeated for credit on each new topic.Prerequisites: none
Credits: 1-3
Independent readings in mathematics under the direction of a graduate faculty member.Prerequisites: none
Credits: 3
Examination of cognitive theories guiding research in mathematics education; analysis and interpretation of research procedures applied in experimental, qualitative, program evaluation, survey, meta-analysis, theory-generating, and action research studies in mathematics education.Prerequisites: none
Credits: 1-4
A course designed to upgrade the qualifications of persons on-the-job. May be repeated for credit on each new topic.Prerequisites: none
Credits: 3
Topological spaces, continuity, product spaces, connectedness, separation, compactness, and metric spaces.Prerequisites: none
Credits: 3
Will cover topics at the discretion of the instructor, such as, but not limited to, those in the following list: algebraic topology, homotopy theory, homology theory, differential topology, topological groups, topological vector spaces, categorical topology, catastrophe theory, lie Groups, algebras of continuous functions, and uniform structures.Prerequisites: none
Credits: 1-2
Research under the supervision of the student's advisor leading to an alternate plan paper.Prerequisites: none
Credits: 1-2
Research under the supervision of the student's advisor leading to an alternate plan paper.Prerequisites: none
Credits: 1-4
A short course devoted to a specific mathematical topic. May be repeated for credit on each new topic.Prerequisites: none
Credits: 3
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 696Prerequisites: none
Credits: 1-12
Provides a student the opportunity to gain expertise and experience in a special field under the supervision of a qualified person.Prerequisites: none
Credits: 1-12
Provides a student the opportunity to gain expertise and experience in a special field under the supervision of a qualified person.Prerequisites: none
Credits: 1-4
Research under the supervision of the student's advisor leading to a thesis.Prerequisites: none
Credits: 1-4
Research under the supervision of the student's advisor leading to a thesis.Prerequisites: none
