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Mathematics & Statistics (MATH) University of New Hampshire 1 MATH 831 - Mathematics for Geodesy MATHEMATICS & STATISTICS Credits: 3 A survey of topics from undergraduate mathematics designed for (MATH) graduate students in engineering and science interested in applications to geodesy and Earth Sciences. Topics include essential elements from # Course numbers with the # symbol included (e.g. #400) have not analytic geometry, geometry of surfaces, linear algebra and statistics, been taught in the last 3 years. Fourier analysis, discrete Fourier transforms and software, filtering applications to tidal data. MATH 801 - Exploring Mathematics for Teachers I Prerequisite(s): (MATH 645 with a minimum grade of D- or MATH 645H Credits: 3 with a minimum grade of D- or MATH 762 with a minimum grade of D- or Provides prospective elementary teachers with the opportunity to MATH 862 with a minimum grade of B-). explore and master concepts involving number systems and operations, Grade Mode: Letter Grade data analysis and probability. Additional topics may include geometry, MATH 832 - Introduction to the R Software measurement, and algebraic thinking. Mathematical reasoning, problem Credits: 1 solving, and the use of appropriate manipulatives and technology are This course provides a basic introduction to the open-sources statistical integrated throughout the course. Readings, class discussions, and software R for students who have never used this software or have never assignments focus on mathematics content as well as applicable formally learned the basics of it. Topics include: Numeric calculations, theories of learning, curriculum resources, and state and national simple and advanced graphics, object management and work-flow, recommendations. The course models instructional techniques that can RStudio, user-contributed packages, basic programming, writing of be adapted to the elementary curricula. Credit offered only to M.Ed. and functions, statistical modeling and related graphs, distributed computing, M.A.T., certificate students, and in-service teachers. (Not offered for credit reproducible research and document production via markup language. if credit is received for MATH #821 or MATH 823.) Cr/F. Prerequisite(s): (EDUC 500 with a minimum grade of D- or EDUC 935 with Equivalent(s): MATH 859 a minimum grade of B-). Grade Mode: Equivalent(s): MATH 601, MATH #821, MATH 823 Grade Mode: Letter Grade MATH 835 - Statistical Methods for Research Credits: 3 MATH #821 - Number Systems for Teachers This course provides a solid grounding in modern applications of Credits: 3 statistics to a wide range of disciplines by providing an overview of the Ways of representing numbers, relationships between numbers, number fundamental concepts of statistical inference and analysis, including t- systems, the meanings of operations and how they relate to one another, tests and confidence intervals. Additional topics include: ANOVA, multiple and computation with number systems as a foundation for algebra; linear regression, analysis of cross classified categorical data, logistic episodes in history and development of the number system; and regression, nonparametric statistics and data mining using CART. The examination of the developmental sequence and learning trajectory as use of statistical software, such as JMP. S PLUS, or R, is fully integrated children learn number concepts. Credit offered only to M.Ed., M.A..T., into the course. Elementary Math Specialist certificate only students, and in-service Grade Mode: Letter Grade teachers. Not offered for credit if credit received for MATH 621. Equivalent(s): MATH 621 MATH 836 - Advanced Statistical Methods for Research Grade Mode: Letter Grade Credits: 3 An introduction to multivariate statistical methods, including principal MATH 823 - Statistics and Probability for Teachers components, discriminant analysis, cluster analysis, factor analysis, Credits: 3 multidimensional scaling, and MANOVA. Additional topics include An introduction to probability, descriptive statistics and data analysis; generalized linear models, general additive models, depending on the exploration of randomness, data representation and modeling. interests of class participants. This course completes a solid grounding Descriptive statistics will include measures of central tendency, in modern applications of statistics used in most research applications. dispersion, distributions and regression. Analysis of experiments The use of statistical software, such as JMP, S PLUS, or R, is fully requiring hypothesizing, experimental design and data gathering. Credit integrated into the course. offered only to M.Ed., M.A..T., Elementary Math Specialist certificate only Prerequisite(s): (MATH 835 with a minimum grade of B- or MATH 839 students, and in-service teachers. Not offered for credit if credit received with a minimum grade of B-). for MATH 623. Grade Mode: Letter Grade Prerequisite(s): (MATH 621 with a minimum grade of D- or MATH #821 with a minimum grade of B-). Equivalent(s): MATH 623 Grade Mode: Letter Grade 2 Mathematics & Statistics (MATH) MATH 837 - Statistical Methods for Quality Improvement and Design MATH 841 - Survival Analysis Credits: 3 Credits: 3 Six Sigma is a popular, data-focused methodology used worldwide by Explorations of models and data-analytic methods used in medical, organizations to achieve continuous improvement of their existing biological, and reliability studies. Event-time data, censored data, processes, products and services or to design new ones. This course reliability models and methods, Kaplan-Meier estimator, proportional provides a thorough introduction to the Six Sigma principles, methods, hazards, Poisson models, loglinear models. The use of statistical and applications for continuous improvement (DMAIC process) and an software, such as SAS, JMP, or R, is fully integrated into the course. overview of Design for Six Sigma (DFSS). Both manufacturing and non- Prereq: MATH 839. (Offered in alternate years.) manufacturing (transactional Six Sigma) applications will be included. Grade Mode: Letter Grade Emphasis is placed on the use of case studies to motivate the use of, as MATH 843 - Time Series Analysis well as the proper application of, the Six Sigma methodology. Formal Six Credits: 3 Sigma Green Belt certification from UNH may be attained by successfully An introduction to univariate time series models and associated methods completing TECH 696. Students must have completed a calculus-based of data analysis and inference in the time domain and frequency domain. introductory statistics course. Topics include: Auto regressive (AR), moving average (MA), ARMA and Grade Mode: Letter Grade ARIMA processes, stationary and non-stationary processes, seasonal MATH 838 - Data Mining and Predictive Analytics ARIMA processes, auto-correlation and partial auto-correlation functions, Credits: 3 identification of models, estimation of parameters, diagnostic checking An introduction to supervised and unsupervised methods for exploring of fitted models, forecasting, spectral density function, periodogram and large data sets and developing predictive models. Unsupervised methods discrete Fourier transform, linear filters. parametric spectral estimation, include: market basket analysis, principal components, clustering, dynamic Fourier analysis. Additional topics may include wavelets and and variables clustering. Important statistical and machine learning long memory processes (FARIMA) and GARCH Models. The use of methods (supervised learning) include: Classification and Regression statistical software, such as JMP, or R, is fully integrated in to the course. Tress (CART), Random Forests, Neural Nets, Support Vector Machines, Offered in alternate years in the spring. Logistic Regression and Penalized Regression. Additional topics focus Prerequisite(s): (MATH 835 with a minimum grade of B- or MATH 839 on metamodeling, validation strategies, bagging and boosting to improve with a minimum grade of B-). prediction or classification, and ensemble prediction from a set of diverse Grade Mode: Letter Grade models. Required case studies and projects provide students with MATH 844 - Design of Experiments II experience in applying these techniques and strategies. The course Credits: 3 necessarily involves the use of statistical software and programming Second course in design of experiments, with applications in quality languages. Students must have completed a calculus-based introductory improvement and industrial manufacturing, engineering research and statistics course. development, research in physical and biological sciences. Covers Grade Mode: Letter Grade experimental design strategies and issues that are often encountered in MATH 839 - Applied Regression Analysis practice complete and incomplete blocking, partially balanced incomplete Credits: 3 blocking (PBIB), partial confounding, intra and inter block information, Statistical methods for the analysis of relationships between response split plotting and strip plotting, repeated measures, crossover designs, and input variables: simple linear regression, multiple regression analysis, Latin squares and rectangles, Youden squares, crossed and nested residual analysis model selection, multi-collinearity, nonlinear curve treatment structures, variance components, mixed effects models, fitting, categorical predictors, introduction to analysis of variance, analysis of covariance, optimizations, space filling designs, and modern analysis of covariance, examination of validity of underlying assumptions, screening design
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