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  • 15 067: Competitive Decision-Making and Negotiation

    3.00 Credits
    Massachusetts Institute of Technology

    Learn tools to achieve negotiation objectives fairly and responsibly. Negotiation skills developed by active participation in a variety of negotiation settings: an oil price (repetitive Prisoners' Dilemma) negotiation; fair division of a valuable art collection and a series of integrative bargaining cases between two and more than two parties over multiple issues; e.g. owners of an online vendor of mid-priced wines negotiates sale of the company to a large chain; two companies negotiate an IT deal. Several complex team negotiations follow. Grades depend solely on effective negotiation with class counterparts. Students must complete all negotiation exercises in order to receive a grade. Prerequisite:    Prereq: None
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  • 15 068: Statistical Consulting

    3.00 Credits
    Massachusetts Institute of Technology

    Addresses statistical issues as a consultant would face them: deciphering the client's question; finding appropriate data; performing a viable analysis; and presenting the results in compelling ways. Real-life cases and examples. Prerequisite:    Prereq: 15.060
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  • 15 070J: Advanced Stochastic Processes

    3.00 Credits
    Massachusetts Institute of Technology

    Analysis and modeling of stochastic processes. Topics include measure theoretic probability, martingales, filtration, and stopping theorems; elements of large deviations theory; Brownian motion and reflected Brownian motion; stochastic integration and Ito calculus; functional limit theorems. Applications to finance theory, insurance, queueing and inventory models. Prerequisite:    Prereq: 6.431, 15.085J, or 18.100
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  • 15 071: The Analytical Edge

    3.00 Credits
    Massachusetts Institute of Technology

    Presents real-world examples in which quantitative methods provide a significant competitive edge that has led to a first order impact on a variety of some of today's most important companies. Examples include finance (quantitative asset management and options pricing), sports, health care, revenue management, supply chains, and the Internet. Outlines the competitive landscape, presents the key quantitative methods that created the edge (data-mining, dynamic optimization, simulation), and discusses the impact of these methods. Includes team projects. Prerequisite:    Prereq: 15.053 or 15.060
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  • 15 072J: Queues: Theory and Applications

    3.00 Credits
    Massachusetts Institute of Technology

    Modeling and analysis of queueing systems, with applications in communications, manufacturing, computers, call centers, service industries and transportation. Topics include birth-death processes and simple Markovian queues, networks of queues and product form networks, single and multi-server queues, multi-class queueing networks, fluid models, adversarial queueing networks, heavy-traffic theory and diffusion approximations. Covers state of the art results which lead to research opportunities. Prerequisite:    Prereq: 6.262
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  • 15 073J: Logistical and Transportation Planning Methods

    3.00 Credits
    Massachusetts Institute of Technology

    Quantitative techniques of operations research with emphasis on applications in transportation systems analysis (urban, air, ocean, highway, and pickup and delivery systems) and in the planning and design of logistically oriented urban service systems (e.g., fire and police departments, emergency medical services, and emergency repair services). Unified study of functions of random variables, geometrical probability, multi-server queuing theory, spatial location theory, network analysis and graph theory, and relevant methods of simulation. Computer exercises and discussions of implementation difficulties. Prerequisite:    Prereq: 6.041
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  • 15 074J: Statistical Reasoning and Data Modeling

    4.00 Credits
    Massachusetts Institute of Technology

    Designed for students who have some acquaintance with probability and/or statistics and want exposure to a wider range of topics and examples. Begins with a brief review of statistics and regression by addressing advanced topics, such as variable selection, data and regression diagnostics, visualization, and Bayesian and robust methods. The remainder starts with data-mining, including stratified sampling, classification, logistic regression, and clustering. These topics are followed by time series analysis and forecasting, design of experiments and analysis of variance, and process control. Students use statistical computing systems, including Excel add-ins and stand-alone packages. Includes case studies involving finance, management science, consulting, and engineering systems. Prerequisite:    Prereq: 6.431, 15.060, or permission of instructor
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  • 15 075J: Statistical Thinking and Data Analysis

    4.00 Credits
    Massachusetts Institute of Technology

    Introduces statistical data analysis. Topics chosen from applied probability, sampling, estimation, hypothesis testing, linear regression, analysis of variance, categorical data analysis, and nonparametric statistics. Prerequisite:    Prereq: 6.041 or 18.440
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  • 15 077J: Statistical Learning and Data Mining

    4.00 Credits
    Massachusetts Institute of Technology

    Advanced introduction to the theory and application of statistics and data-mining, concentrating on techniques used in management science, finance, consulting, engineering systems, and bioinformatics. First half builds the statistical foundation for the second half, with topics selected from sampling, including the bootstrap, theory of estimation, testing, nonparametric statistics, analysis of variance, categorical data analysis, regression analysis, MCMC, EM, Gibbs sampling, and Bayesian methods. Second half focuses on data-mining, supervised learning, and multivariate analysis. Topics selected from logistic regression; principal components and dimension reduction; discrimination and classification analysis, including trees (CART), partial least squares, nearest neighbor and regularized methods, support vector machines, boosting and bagging, clustering, independent component analysis, and nonparametric regression. Uses statistics software packages, such as R, S+, MATLAB, and SAS, for data analysis and data-mining. Prerequisite:    Prereq: 6.431, 15.085, or 18.440; 18.06 or 18.700
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  • 15 078J: Models, Data and Inference for Socio-Technical Systems

    3.00 Credits
    Massachusetts Institute of Technology

    Use data and systems knowledge to build models of complex socio-technical systems for improved system design and decision-making. Enhance model-building skills, including: review and extension of functions of random variables, Poisson processes, and Markov processes. Move from applied probability to statistics via Chi-squared t and f tests, derived as functions of random variables. Review classical statistics, hypothesis tests, regression, correlation and causation, simple data mining techniques, and Bayesian vs. classical statistics. Class project. Prerequisite:    Prereq: ESD.83, 6.041, or permission of instructor
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