(3 Credits). The student registers for the section number of his/her advisor. (3 credits) The course is a self-contained rigorous measure-theoretic introduction to probability theory. (3 Credits). Students perform an interdisciplinary team project using systems safety engineering methods to redesign a work station, manufacturing process, or consumer product. Statistics 817: Interdisciplinary Seminar in Quantitative Social Science Methodology (EDUC 817/PSYCH 817/SOC 810). Advisory Pre-requisite: Students should have a strong preparation in either biology or some branch of quantitative analysis (mathematics, statistics, or computer science), but not necessarily in both domains. Assembly Modeling for Design and Manufacturing Prerequisites: STATS 510 and 511 or equivalent, real analysis (Math 451 or equivalent). On the other hand, an average Joe may use soccer stats such as Over 2.5 or BTTS % to predict the outcome of an upcoming match. The course will cover efficient algorithms for network optimization problems, basics of dynamic programming and convex programming, and advanced topics in linear and integer programming. It also covers issues related to data collection, study design, and interpretation of findings, including missing data, non-representative samples, causality, and designed experiments. Enforced Pre-Requisite: No credit in STATS 513. Decomposition principle, generalized linear programs. Recent developments in the foundations and methodology of sampling finite populations. The focus is on providing a strong foundation for both further study and practice in the field of risk analysis.
(58 Documents). CourseProfile (ATLAS), IOE 413. The methodologies include mixed-integer programming and convex optimization, Benders decomposition, Column Generation, and constraint programming.
CourseProfile (ATLAS), IOE 424.
This course provides students with hands-on experience using a variety of techniques from modern applied statistics through case studies involving data drawn from various fields. Work Organizations
Development of the simplex algorithm; duality theory and economic interpretations. Prerequisite: IOE 333 and IOE 366.
Statistical Learning I: Regression --- The course covers concepts and methods for regression analysis and applications. Circulation and feasibility theorems. You can learn more about this at. Methods of direct search, Newton and Quasi-Newton, gradient projection, feasible direction, reduced gradient; solution methods for nonlinear equations. The art and science of developing, using and explicating mathematical models, presented in a studio/workshop environment. Statistics 500: Statistical Learning I: Regression, The course covers concepts and methods for regression analysis and applications.
Auto body assembly case studies. Statistics 560: Introduction to Nonparametric Statistics (BIOS 685), Confidence intervals and tests for quantiles, tolerance regions, and coverages; estimation by U statistics and linear combination or order statistics; large sample theory for U statistics and order statistics; the sample distribution and its uses including goodness-of-fit tests; rank and permutation tests for several hypotheses including a discussion of locally most powerful rank and permutation tests; and large sample and asymptotic efficiency for selected tests. CourseProfile (ATLAS), IOE 437. Topics include: (1) classification and machine learning, including support vector machines, recursive partitioning, and ensemble methods; (2) methods for analyzing sets of curves, surfaces and images, including functional data analysis, wavelets, independent component analysis, and random field models; (3) modern regression, including splines and generalized additive models, (4) methods for analyzing structured dependent data, including mixed effects models, hierarchical models, graphical models, and Bayesian networks; and (5) clustering, detection, and dimension reduction methods, including manifold learning, spectral clustering, and bump hunting. Stochastic Analysis for Finance Student must register for the section number of the instructor/advisor. CourseProfile (ATLAS), IOE 615.
A global supply network consists of “arcs” (transportation) and “nodes” (facilities) such as container terminals, crossdocks, distribution centers, consolidation centers, railway yards, and airfreight terminals.
Topics to be covered include: (i) constructions of probability spaces, Kolmogorov's consistency theorem; independence of families of random variables, Borel-Cantelli lemmas and 0-1 laws; (ii) various modes of convergence (in probability, almost surely, in Lp, in distribution) and properties of weak convergence, (iii) laws of large numbers, (iv) central limit theorems for sequences and triangular arrays, (v) conditional expectations and distributions and (vi) discrete time martingale theory. Material Handling Systems We proceed to develop the implications of no arbitrage in dynamic trading models: the binomial and Black-Scholes models. - Prerequisite: Math 316 and Math 425/525 or IOE 552. Market share of each team is determined through both a web-based competition and a physical trade show. (3 credits) Graduate standing.
(3 Credits), Pre-requisite: STATS 425 and 426 (or IOE 316 and 366), Statistics 545: Data Analysis in Molecular Biology (BIOSTAT 646, BIOINFORMATICS 545), The course will cover statistical methods used to analyze data in experimental molecular biology, with an emphasis on gene and protein expression array data. Provide insight into leading theories concerning the administration of research and industrial organizations. The firm has grown to a family of over 23 funds and has invested in over 2400 companies internationally. Instruction Mode: In-Person – Asynchronous, Online – Asynchronous Fall 2020, AIC, Mallows' Cp, adjusted R2, ridge regression, and Lasso can all help to deal with overfitting. Introduction to non-measure theoretic stochastic processes. Prerequisite: IOE 510 (Math 561). Problems with computer displays, illumination, noise, eye-hand coordination as well as repetitive and high physical effort tasks are presented. Geometric Dimensioning & Tolerancing. (3 credits). (3 credits)
The response variable could be continuous, binary or counts. Applications of these models in key scientific and engineering areas, such as genetics, epidemics, computational algorithms, computer and communications networks, inventory systems financial and risk management, are discussed. Application of these stochastic models and the “physics” of flow operations with emphasis on services, healthcare, and production. General knowledge of probability theory and stochastic processes is assumed. (3 Credits). This is a Tauber Institute-sponsored graduate elective. Interprofessional Perspectives in Occupational Health and Safety
Instruction Mode: Online – Synchronous Prerequisite: Math 217, Math 417, or Math 419. (116 Documents), STATS 401 - Prerequisite: Preceded or accompanied by IOE 265. Models for non-sampling errors including bias, response error and non-response. The problem of scheduling several tasks over time, including the topics of measures of performance, single-machine sequencing, flow shop scheduling, the job shop problem and priority dispatching.
(3 credits) (3 Credits), Statistics 605: Advanced Topics in Modeling and Data Analysis, This course covers recent developments in statistical modeling and data analysis. Occupational Safety Management Pre-requisites: MATH 451 or equivalent knowledge of real analysis. Introduction to transportation and assignment problems; special purpose algorithms and advanced computational techniques. Instruction Mode: Online – Synchronous This course covers recent developments in statistical theory. - Lean Manufacturing and Services (MFG 426) (3 credits, no credit granted for students who have credit for MATH 423) We've done the research into which leagues have the most winning potential.
Instruction Mode: Online – Synchronous The bulk of this course focuses on stochastic models that capture the evolution in time of various random phenomena and/or dynamical systems. Pre-requisites: MATH 417 and either STATS 611 or BIOSTAT 602. Design of lean manufacturing systems requires knowledge and skills for describing manual work, identifying value and non-value added work elements, designing efficient work equipment and methods, preventing fatigue and related worker health problems and predicting work performance. Students will be expected to complete a course project and present to group. CourseProfile (ATLAS), IOE 712.
(3 credits) Time series modeling, analysis, forecasting and control, identifying parametric time series, autovariance, spectra, Green’s function, trend and seasonality. Introductory graduate course on optimization methods in supply chain management. (3 credits)
Topics include dimension reduction techniques, including principal component analysis, factor analysis, multidimensional scaling and manifold learning; conceptual framework of classification including cost functions, Bayes classifiers, overfitting and generalization; specific classification methods including logistic regression, naive Bayes, discriminant analysis, support vector machines, kernel-based methods, generalized additive models, tree-based methods, boosting, neural networks; clustering methods including K-means, model-based clustering algorithms, mixture models, latent variable models, hierarchical models; and algorithms such as the EM algorithm, Gibbs sampling, and variational inference methods. (2 credits) (7-week course)
Pre-requisite: STATS Master's Standing or stats 500. CourseProfile (ATLAS), IOE 613.
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