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Description

This course explores the basic concepts of computer modeling and simulation in science and engineering. We'll use techniques and software for simulation, data analysis and visualization. Continuum, mesoscale, atomistic and quantum methods are used to study fundamental and applied problems in physics, chemistry, materials science, mechanics, engineering, and biology. Examples drawn from the disciplines above are used to understand or characterize complex structures and materials, and complement experimental observations. This course explores the basic concepts of computer modeling and simulation in science and engineering. We'll use techniques and software for simulation, data analysis and visualization. Continuum, mesoscale, atomistic and quantum methods are used to study fundamental and applied problems in physics, chemistry, materials science, mechanics, engineering, and biology. Examples drawn from the disciplines above are used to understand or characterize complex structures and materials, and complement experimental observations.Subjects

computer modeling | computer modeling | discrete particle system | discrete particle system | continuum | continuum | continuum field | continuum field | statistical sampling | statistical sampling | data analysis | data analysis | visualization | visualization | quantum | quantum | quantum method | quantum method | chemical | chemical | molecular dynamics | molecular dynamics | Monte Carlo | Monte Carlo | mesoscale | mesoscale | continuum method | continuum method | computational physics | computational physics | chemistry | chemistry | mechanics | mechanics | materials science | materials science | biology | biology | applied mathematics | applied mathematics | fluid dynamics | fluid dynamics | heat | heat | fractal | fractal | evolution | evolution | melting | melting | gas | gas | structural mechanics | structural mechanics | FEM | FEM | finite element | finite elementLicense

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htmSite sourced from

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See all metadata15.075 Applied Statistics (MIT) 15.075 Applied Statistics (MIT)

Description

This course is an introduction to applied statistics and data analysis. Topics include collecting and exploring data, basic inference, simple and multiple linear regression, analysis of variance, nonparametric methods, and statistical computing. It is not a course in mathematical statistics, but provides a balance between statistical theory and application. Prerequisites are calculus, probability, and linear algebra. We would like to acknowledge the contributions that Prof. Roy Welsch (MIT), Prof. Gordon Kaufman (MIT), Prof. Jacqueline Telford (Johns Hopkins University), and Prof. Ramón León (University of Tennessee) have made to the course material. This course is an introduction to applied statistics and data analysis. Topics include collecting and exploring data, basic inference, simple and multiple linear regression, analysis of variance, nonparametric methods, and statistical computing. It is not a course in mathematical statistics, but provides a balance between statistical theory and application. Prerequisites are calculus, probability, and linear algebra. We would like to acknowledge the contributions that Prof. Roy Welsch (MIT), Prof. Gordon Kaufman (MIT), Prof. Jacqueline Telford (Johns Hopkins University), and Prof. Ramón León (University of Tennessee) have made to the course material.Subjects

data analysis | data analysis | multiple regression | multiple regression | analysis of variance | analysis of variance | multivariate analysis | multivariate analysis | data mining | data mining | probability | probability | collecting data | collecting data | sampling distributions | sampling distributions | inference | inference | linear regression | linear regression | ANOVA | ANOVA | nonparametric methods | nonparametric methods | polls | polls | surveys | surveys | statistics | statistics | management science | management science | finance | finance | statistical graphics | statistical graphics | estimation | estimation | hypothesis testing | hypothesis testing | logistic regression | logistic regression | contingency tables | contingency tables | forecasting | forecasting | factor analysis | factor analysisLicense

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htmSite sourced from

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This team taught, multidisciplinary course covers the fundamentals of magnetic resonance imaging relevant to the conduct and interpretation of human brain mapping studies. The challenges inherent in advancing our knowledge about brain function using fMRI are presented first to put the work in context. The course then provides in depth coverage of the physics of image formation, mechanisms of image contrast, and the physiological basis for image signals. Parenchymal and cerebrovascular neuroanatomy and application of sophisticated structural analysis algorithms for segmentation and registration of functional data are discussed. Additional topics include fMRI experimental design including block design, event related and exploratory data analysis methods, building and applying statistical mod This team taught, multidisciplinary course covers the fundamentals of magnetic resonance imaging relevant to the conduct and interpretation of human brain mapping studies. The challenges inherent in advancing our knowledge about brain function using fMRI are presented first to put the work in context. The course then provides in depth coverage of the physics of image formation, mechanisms of image contrast, and the physiological basis for image signals. Parenchymal and cerebrovascular neuroanatomy and application of sophisticated structural analysis algorithms for segmentation and registration of functional data are discussed. Additional topics include fMRI experimental design including block design, event related and exploratory data analysis methods, building and applying statistical modSubjects

medical imaging | medical imaging | medical lab | medical lab | medical technology | medical technology | magnetic resonance imaging | magnetic resonance imaging | fMRI | fMRI | signal processing | signal processing | human brain mapping | human brain mapping | function | function | image formation physics | image formation physics | metabolism | metabolism | psychology | psychology | image signals | image signals | parenchymal | parenchymal | cerebrovascular neuroanatomy | cerebrovascular neuroanatomy | functional data analysis | functional data analysis | experimental design | experimental design | statistical models | statistical models | human subjects | human subjects | informed consent | informed consent | institutional review board requirements | institutional review board requirements | safety | safety | medical | medical | brain scan | brain scanLicense

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htmSite sourced from

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This course surveys the basic concepts of computer modeling in science and engineering using discrete particle systems and continuum fields. It covers techniques and software for statistical sampling, simulation, data analysis and visualization, and uses statistical, quantum chemical, molecular dynamics, Monte Carlo, mesoscale and continuum methods to study fundamental physical phenomena encountered in the fields of computational physics, chemistry, mechanics, materials science, biology, and applied mathematics. Applications are drawn from a range of disciplines to build a broad-based understanding of complex structures and interactions in problems where simulation is on equal footing with theory and experiment. A term project allows development of individual interests. Students are mentor This course surveys the basic concepts of computer modeling in science and engineering using discrete particle systems and continuum fields. It covers techniques and software for statistical sampling, simulation, data analysis and visualization, and uses statistical, quantum chemical, molecular dynamics, Monte Carlo, mesoscale and continuum methods to study fundamental physical phenomena encountered in the fields of computational physics, chemistry, mechanics, materials science, biology, and applied mathematics. Applications are drawn from a range of disciplines to build a broad-based understanding of complex structures and interactions in problems where simulation is on equal footing with theory and experiment. A term project allows development of individual interests. Students are mentorSubjects

computer modeling | computer modeling | discrete particle system | discrete particle system | continuum | continuum | continuum field | continuum field | statistical sampling | statistical sampling | data analysis | data analysis | visualization | visualization | quantum | quantum | quantum method | quantum method | chemical | chemical | molecular dynamics | molecular dynamics | Monte Carlo | Monte Carlo | mesoscale | mesoscale | continuum method | continuum method | computational physics | computational physics | chemistry | chemistry | mechanics | mechanics | materials science | materials science | biology; applied mathematics | biology; applied mathematics | fluid dynamics | fluid dynamics | heat | heat | fractal | fractal | evolution | evolution | melting | melting | gas | gas | structural mechanics | structural mechanics | FEM | FEM | finite element | finite element | biology | biology | applied mathematics | applied mathematics | 1.021 | 1.021 | 2.030 | 2.030 | 3.021 | 3.021 | 10.333 | 10.333 | 18.361 | 18.361 | HST.588 | HST.588 | 22.00 | 22.00License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htmSite sourced from

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This course involves reading about how to do fieldwork, practicing fieldwork, reading ethnographies and about ethnography, and practicing writing ethnography. We will move from an overview of ethnography, to getting into the field, to writing fieldnotes, to analyzing data and writing a short ethnographic piece.We will, as you must in doing fieldwork and writing ethnographies, intersperse reading with fieldwork to theoretically inform both the fieldwork and the writing. The ethics of fieldwork and obligations to research subjects are discussed throughout the semester.  This course involves reading about how to do fieldwork, practicing fieldwork, reading ethnographies and about ethnography, and practicing writing ethnography. We will move from an overview of ethnography, to getting into the field, to writing fieldnotes, to analyzing data and writing a short ethnographic piece.We will, as you must in doing fieldwork and writing ethnographies, intersperse reading with fieldwork to theoretically inform both the fieldwork and the writing. The ethics of fieldwork and obligations to research subjects are discussed throughout the semester. Subjects

fieldwork | fieldwork | anthropology | anthropology | ethnography | ethnography | culture | culture | theory | theory | data analysis | data analysis | research design | research design | inerviewing | inerviewing | method | method | anthropological field work | anthropological field work | fieldnotes | fieldnotes | ethnographic writing | ethnographic writing | reflexive analysis | reflexive analysis | epistemology | epistemologyLicense

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htmSite sourced from

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This team taught, multidisciplinary course covers the fundamentals of magnetic resonance imaging relevant to the conduct and interpretation of human brain mapping studies. The challenges inherent in advancing our knowledge about brain function using fMRI are presented first to put the work in context. The course then provides in depth coverage of the physics of image formation, mechanisms of image contrast, and the physiological basis for image signals. Parenchymal and cerebrovascular neuroanatomy and application of sophisticated structural analysis algorithms for segmentation and registration of functional data are discussed. Additional topics include fMRI experimental design including block design, event related and exploratory data analysis methods, building and applying statistical mod This team taught, multidisciplinary course covers the fundamentals of magnetic resonance imaging relevant to the conduct and interpretation of human brain mapping studies. The challenges inherent in advancing our knowledge about brain function using fMRI are presented first to put the work in context. The course then provides in depth coverage of the physics of image formation, mechanisms of image contrast, and the physiological basis for image signals. Parenchymal and cerebrovascular neuroanatomy and application of sophisticated structural analysis algorithms for segmentation and registration of functional data are discussed. Additional topics include fMRI experimental design including block design, event related and exploratory data analysis methods, building and applying statistical modSubjects

medical lab | medical lab | medical technology | medical technology | magnetic resonance imaging | magnetic resonance imaging | fMRI | fMRI | signal processing | signal processing | human brain mapping | human brain mapping | function | function | image formation physics | image formation physics | metabolism | metabolism | psychology | psychology | image signals | image signals | parenchymal | parenchymal | cerebrovascular neuroanatomy | cerebrovascular neuroanatomy | functional data analysis | functional data analysis | experimental design | experimental design | statistical models | statistical models | human subjects | human subjects | informed consent | informed consent | institutional review board requirements | institutional review board requirements | safety | safety | medical | medical | brain scan | brain scanLicense

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htmSite sourced from

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This team taught, multidisciplinary course covers the fundamentals of magnetic resonance imaging relevant to the conduct and interpretation of human brain mapping studies. The challenges inherent in advancing our knowledge about brain function using fMRI are presented first to put the work in context. The course then provides in depth coverage of the physics of image formation, mechanisms of image contrast, and the physiological basis for image signals. Parenchymal and cerebrovascular neuroanatomy and application of sophisticated structural analysis algorithms for segmentation and registration of functional data are discussed. Additional topics include fMRI experimental design including block design, event related and exploratory data analysis methods, building and applying statistical mod This team taught, multidisciplinary course covers the fundamentals of magnetic resonance imaging relevant to the conduct and interpretation of human brain mapping studies. The challenges inherent in advancing our knowledge about brain function using fMRI are presented first to put the work in context. The course then provides in depth coverage of the physics of image formation, mechanisms of image contrast, and the physiological basis for image signals. Parenchymal and cerebrovascular neuroanatomy and application of sophisticated structural analysis algorithms for segmentation and registration of functional data are discussed. Additional topics include fMRI experimental design including block design, event related and exploratory data analysis methods, building and applying statistical modSubjects

lab | lab | magnetic resonance imaging | magnetic resonance imaging | human brain mapping | human brain mapping | function | function | image formation physics | image formation physics | psychology | psychology | image signals | image signals | parenchymal | parenchymal | cerebrovascular neuroanatomy | cerebrovascular neuroanatomy | functional data analysis | functional data analysis | experimental design | experimental design | statistical models | statistical modelsLicense

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htmSite sourced from

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This course focuses on the basic concepts of computer modeling in science and engineering using discrete particle systems and continuum fields. Techniques and software for statistical sampling, simulation, data analysis and visualization. Use of statistical, quantum chemical, molecular dynamics, Monte Carlo, mesoscale and continuum methods to study fundamental physical phenomena encountered in the fields of computational physics, chemistry, mechanics, materials science, biology, and applied mathematics. Applications drawn from a range of disciplines to build a broad-based understanding of complex structures and interactions in problems where simulation is on equal-footing with theory and experiment. Term project allows development of individual interest. Student mentoring by a coordinated This course focuses on the basic concepts of computer modeling in science and engineering using discrete particle systems and continuum fields. Techniques and software for statistical sampling, simulation, data analysis and visualization. Use of statistical, quantum chemical, molecular dynamics, Monte Carlo, mesoscale and continuum methods to study fundamental physical phenomena encountered in the fields of computational physics, chemistry, mechanics, materials science, biology, and applied mathematics. Applications drawn from a range of disciplines to build a broad-based understanding of complex structures and interactions in problems where simulation is on equal-footing with theory and experiment. Term project allows development of individual interest. Student mentoring by a coordinatedSubjects

Quantum | Quantum | Modeling | Modeling | visualization | visualization | data analysis | data analysis | simulation | simulation | statistical sampling | statistical sampling | 22.00 | 22.00 | 1.021 | 1.021 | 3.021 | 3.021 | 10.333 | 10.333 | 18.361 | 18.361 | 2.030 | 2.030License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htmSite sourced from

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This seminar explores the development and application of qualitative research designs and methods in political analysis. It considers a broad array of approaches, from exploratory narratives to focused-comparison case studies, for investigating plausible alternative hypotheses. The focus is on analysis, not data collection. This seminar explores the development and application of qualitative research designs and methods in political analysis. It considers a broad array of approaches, from exploratory narratives to focused-comparison case studies, for investigating plausible alternative hypotheses. The focus is on analysis, not data collection.Subjects

development and application of qualitative research designs and methods in political analysis | development and application of qualitative research designs and methods in political analysis | exploratory narrative | exploratory narrative | focused-comparison case studies | focused-comparison case studies | investigating plausible alternative hypotheses | investigating plausible alternative hypotheses | research methods | research methods | methodology | methodology | rival hypothesis | rival hypothesis | research designs | research designs | plausibility | plausibility | political analysis | political analysis | data analysis | data analysis | validity | validity | reliability | reliability | inference | inference | observations | observations | cases | cases | subjects | subjects | research agenda | research agenda | qualitative methods | qualitative methods | qualitative research | qualitative researchLicense

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htmSite sourced from

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Includes audio/video content: AV special element video. This course introduces the concepts, techniques, and devices used to measure engineering properties of materials. There is an emphasis on measurement of load-deformation characteristics and failure modes of both natural and fabricated materials. Weekly experiments include data collection, data analysis, and interpretation and presentation of results. Includes audio/video content: AV special element video. This course introduces the concepts, techniques, and devices used to measure engineering properties of materials. There is an emphasis on measurement of load-deformation characteristics and failure modes of both natural and fabricated materials. Weekly experiments include data collection, data analysis, and interpretation and presentation of results.Subjects

materials laboratory | materials laboratory | load-deformation characteristics | load-deformation characteristics | failure modes | failure modes | experiments | experiments | data collection | data collection | data analysis | data analysis | tension | tension | elastic behavior | elastic behavior | direct shear | direct shear | friction | friction | concrete | concrete | early age properties | early age properties | compression | compression | directionality | directionality | soil classification | soil classification | consolidation test | consolidation test | heat treatment | heat treatmentLicense

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htmSite sourced from

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This subject is a computer-oriented introduction to probability and data analysis. It is designed to give students the knowledge and practical experience they need to interpret lab and field data. Basic probability concepts are introduced at the outset because they provide a systematic way to describe uncertainty. They form the basis for the analysis of quantitative data in science and engineering. The MATLAB® programming language is used to perform virtual experiments and to analyze real-world data sets, many downloaded from the web. Programming applications include display and assessment of data sets, investigation of hypotheses, and identification of possible casual relationships between variables. This is the first semester that two courses, Computing and Data Analysis for Environm This subject is a computer-oriented introduction to probability and data analysis. It is designed to give students the knowledge and practical experience they need to interpret lab and field data. Basic probability concepts are introduced at the outset because they provide a systematic way to describe uncertainty. They form the basis for the analysis of quantitative data in science and engineering. The MATLAB® programming language is used to perform virtual experiments and to analyze real-world data sets, many downloaded from the web. Programming applications include display and assessment of data sets, investigation of hypotheses, and identification of possible casual relationships between variables. This is the first semester that two courses, Computing and Data Analysis for EnvironmSubjects

probability | probability | statistics | statistics | events | events | random variables | random variables | univariate distributions | univariate distributions | multivariate distributions | multivariate distributions | uncertainty propagation | uncertainty propagation | Bernoulli trials | Bernoulli trials | Poisson processed | Poisson processed | conditional probability | conditional probability | Bayes rule | Bayes rule | random sampling | random sampling | point estimation | point estimation | interval estimation | interval estimation | hypothesis testing | hypothesis testing | analysis of variance | analysis of variance | linear regression | linear regression | computational analysis | computational analysis | data analysis | data analysis | environmental engineering | environmental engineering | applications | applications | MATLAB | MATLAB | numerical modeling | numerical modeling | probabilistic concepts | probabilistic concepts | statistical methods | statistical methods | field data | field data | laboratory data | laboratory data | numerical techniques | numerical techniques | Monte Carlo simulation | Monte Carlo simulation | variability | variability | sampling | sampling | data sets | data sets | computer | computer | uncertainty | uncertainty | interpretation | interpretation | quantitative data | quantitative dataLicense

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htmSite sourced from

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See all metadata1.105 Solid Mechanics Laboratory (MIT) 1.105 Solid Mechanics Laboratory (MIT)

Description

This course introduces students to basic properties of structural materials and behavior of simple structural elements and systems through a series of experiments. Students learn experimental technique, data collection, reduction and analysis, and presentation of results. Students generally take this subject during the same semester as 1.050, Solid Mechanics. This course introduces students to basic properties of structural materials and behavior of simple structural elements and systems through a series of experiments. Students learn experimental technique, data collection, reduction and analysis, and presentation of results. Students generally take this subject during the same semester as 1.050, Solid Mechanics.Subjects

properties of structural materials | properties of structural materials | structural elements | structural elements | structural systems | structural systems | experimental technique | experimental technique | data collection | data collection | reduction | reduction | analysis | analysis | presentation | presentation | properties | properties | structural materials | structural materials | structural behavior | structural behavior | simple structural elements | simple structural elements | simple structural systems | simple structural systems | laboratory experiments | laboratory experiments | data reduction | data reduction | data analysis | data analysis | solid mechanics | solid mechanics | loading | loading | observation | observation | measurement | measurement | force | force | displacement | displacement | stiffness | stiffness | failure modes | failure modes | failure mechanisms | failure mechanisms | instrumentation | instrumentation | resolution | resolution | range | range | transducer response | transducer response | signal conditioning | signal conditioning | experimental design | experimental design | report writing | report writingLicense

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htmSite sourced from

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See all metadata5.310 Laboratory Chemistry (MIT) 5.310 Laboratory Chemistry (MIT)

Description

Laboratory Chemistry (5.310) introduces experimental chemistry for students requiring a chemistry laboratory who are not majoring in chemistry. Students must have completed general chemistry (5.111) and have completed or be concurrently enrolled in the first semester of organic chemistry (5.12). The course covers principles and applications of chemical laboratory techniques, including preparation and analysis of chemical materials, measurement of pH, gas and liquid chromatography, visible-ultraviolet spectrophotometry, infrared spectroscopy, kinetics, data analysis, and elementary synthesis. NOTE: The Staff for this course would like to acknowledge that the experiments include contributions from past instructors, course textbooks, and others affiliated with course #5.310. Since the Laboratory Chemistry (5.310) introduces experimental chemistry for students requiring a chemistry laboratory who are not majoring in chemistry. Students must have completed general chemistry (5.111) and have completed or be concurrently enrolled in the first semester of organic chemistry (5.12). The course covers principles and applications of chemical laboratory techniques, including preparation and analysis of chemical materials, measurement of pH, gas and liquid chromatography, visible-ultraviolet spectrophotometry, infrared spectroscopy, kinetics, data analysis, and elementary synthesis. NOTE: The Staff for this course would like to acknowledge that the experiments include contributions from past instructors, course textbooks, and others affiliated with course #5.310. Since theSubjects

lab | lab | chemistry | chemistry | laboratory | laboratory | experiment | experiment | pH | pH | gas chromatography | gas chromatography | liquid chromatography | liquid chromatography | visible-ultraviolet spectrophotometry | visible-ultraviolet spectrophotometry | infrared spectroscopy | infrared spectroscopy | kinetics | kinetics | data analysis | data analysis | elementary synthesis | elementary synthesis | amino acid | amino acid | ferrocene | ferrocene | essential oil | essential oil | potentiometric titration | potentiometric titration | techniques | techniques | measurement | measurement | materials | materials | data | data | analysis | analysis | elementary | elementary | synthesis | synthesis | amino | amino | acid | acid | essential | essential | oil | oil | gas | gas | chromatography | chromatography | infrared | infrared | spectroscopy | spectroscopy | liquid | liquid | potentiometric | potentiometric | titration | titration | visible | visible | ultraviolet | ultraviolet | spectrophotometry | spectrophotometryLicense

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htmSite sourced from

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See all metadata7.90J Computational Functional Genomics (MIT) 7.90J Computational Functional Genomics (MIT)

Description

The course focuses on casting contemporary problems in systems biology and functional genomics in computational terms and providing appropriate tools and methods to solve them. Topics include genome structure and function, transcriptional regulation, and stem cell biology in particular; measurement technologies such as microarrays (expression, protein-DNA interactions, chromatin structure); statistical data analysis, predictive and causal inference, and experiment design. The emphasis is on coupling problem structures (biological questions) with appropriate computational approaches. The course focuses on casting contemporary problems in systems biology and functional genomics in computational terms and providing appropriate tools and methods to solve them. Topics include genome structure and function, transcriptional regulation, and stem cell biology in particular; measurement technologies such as microarrays (expression, protein-DNA interactions, chromatin structure); statistical data analysis, predictive and causal inference, and experiment design. The emphasis is on coupling problem structures (biological questions) with appropriate computational approaches.Subjects

systems biology | systems biology | genome structure | genome structure | DNA | DNA | RNA | RNA | transcription | transcription | stem cell | stem cell | biology | biology | microarray | microarray | gene expression | gene expression | statistical data analysis | statistical data analysis | chromatin | chromatin | gene sequence | gene sequence | genomic sequence | genomic sequence | motif | motif | protein | protein | error model | error model | diagnostic | diagnostic | gene clustering | gene clustering | phenotype | phenotype | clustering | clustering | proteome | proteome | 7.90 | 7.90 | 6.874 | 6.874License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htmSite sourced from

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This course is an introduction to statistical data analysis. Topics are chosen from applied probability, sampling, estimation, hypothesis testing, linear regression, analysis of variance, categorical data analysis, and nonparametric statistics. This course is an introduction to statistical data analysis. Topics are chosen from applied probability, sampling, estimation, hypothesis testing, linear regression, analysis of variance, categorical data analysis, and nonparametric statistics.Subjects

15.075 | 15.075 | ESD.07 | ESD.07 | statistics | statistics | data analysis | data analysis | multiple regression | multiple regression | analysis of variance | analysis of variance | multivariate analysis | multivariate analysis | data mining | data mining | probability | probability | collecting data | collecting data | sampling distributions | sampling distributions | inference | inference | linear regression | linear regression | ANOVA | ANOVA | chi-square test | chi-square testLicense

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htmSite sourced from

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See all metadata15.063 Communicating With Data (MIT) 15.063 Communicating With Data (MIT)

Description

Communicating With Data has a distinctive structure and content, combining fundamental quantitative techniques of using data to make informed management decisions with illustrations of how real decision makers, even highly trained professionals, fall prey to errors and biases in their understanding. We present the fundamental concepts underlying the quantitative techniques as a way of thinking, not just a way of calculating, in order to enhance decision-making skills. Rather than survey all of the techniques of management science, we stress those fundamental concepts and tools that we believe are most important for the practical analysis of management decisions, presenting the material as much as possible in the context of realistic business situations from a variety of settings. Exer Communicating With Data has a distinctive structure and content, combining fundamental quantitative techniques of using data to make informed management decisions with illustrations of how real decision makers, even highly trained professionals, fall prey to errors and biases in their understanding. We present the fundamental concepts underlying the quantitative techniques as a way of thinking, not just a way of calculating, in order to enhance decision-making skills. Rather than survey all of the techniques of management science, we stress those fundamental concepts and tools that we believe are most important for the practical analysis of management decisions, presenting the material as much as possible in the context of realistic business situations from a variety of settings. ExerSubjects

quantitative | quantitative | data analysis | data analysis | graphs | graphs | charts | charts | factual decisions | factual decisions | statistics | statistics | communication | communication | fact-based | fact-based | information analysis | information analysis | spreadsheets | spreadsheets | models | models | probability | probability | decision analysis | decision analysis | regression | regression | simulation | simulation | linear | linear | nonlinear | nonlinear | optimization | optimization | data | data | analysis | analysis | marketing | marketing | finance | finance | operations management | operations management | strategy | strategy | operations | operations | management | management | diagrams | diagrams | formula | formula | structure | structure | content | contentLicense

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htmSite sourced from

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See all metadata15.821 Listening to the Customer (MIT) 15.821 Listening to the Customer (MIT)

Description

The 15.821 and 15.822 Sequence Marketing research may be divided into methods that emphasize understanding "the customer" and methods that emphasize understanding "the market." This course (15.821) deals with the customer and emphasizes qualitative methods (interviews, focus groups, Voice of the Customer, composing questions for a survey). The companion course (15.822) deals with the market and emphasizes quantitative methods (sampling, survey execution, quantitative data interpretation, conjoint analysis, factor analysis). The methods covered in 15.821 are often used in the "front-end" of market research project, whose second-stage is a quantitative survey. The quality of information gathered in the second-stage is greatly enhanced in this way. 15.821 is designed for the nonspeciali The 15.821 and 15.822 Sequence Marketing research may be divided into methods that emphasize understanding "the customer" and methods that emphasize understanding "the market." This course (15.821) deals with the customer and emphasizes qualitative methods (interviews, focus groups, Voice of the Customer, composing questions for a survey). The companion course (15.822) deals with the market and emphasizes quantitative methods (sampling, survey execution, quantitative data interpretation, conjoint analysis, factor analysis). The methods covered in 15.821 are often used in the "front-end" of market research project, whose second-stage is a quantitative survey. The quality of information gathered in the second-stage is greatly enhanced in this way. 15.821 is designed for the nonspecialiSubjects

pricing | pricing | sequence | sequence | marketing research | marketing research | quantitative | quantitative | observation | observation | data analysis | data analysis | segmentation | segmentation | perceptual mapping | perceptual mapping | cluster | cluster | statistics | statisticsLicense

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See all metadata15.822 Strategic Marketing Measurement (MIT) 15.822 Strategic Marketing Measurement (MIT)

Description

Marketing research may be divided into methods that emphasize understanding "the customer" and methods that emphasize understanding "the market." This course (15.822) deals with the market. The companion course (15.821) deals with the customer. The course will teach you how to write, conduct and analyze a marketing research survey. The emphasis will be on discovering market structure and segmentation, but you can pursue other project applications. A major objective of the course is to give you some "hands-on" exposure to analysis techniques that are widely used in consulting and marketing research factor analysis, perceptual mapping, conjoint, and cluster analysis). These techniques used to be considered advanced but now involve just a few keystrokes on most stat software packages. T Marketing research may be divided into methods that emphasize understanding "the customer" and methods that emphasize understanding "the market." This course (15.822) deals with the market. The companion course (15.821) deals with the customer. The course will teach you how to write, conduct and analyze a marketing research survey. The emphasis will be on discovering market structure and segmentation, but you can pursue other project applications. A major objective of the course is to give you some "hands-on" exposure to analysis techniques that are widely used in consulting and marketing research factor analysis, perceptual mapping, conjoint, and cluster analysis). These techniques used to be considered advanced but now involve just a few keystrokes on most stat software packages. TSubjects

marketing research | marketing research | quantitative | quantitative | data analysis | data analysis | segmentation | segmentation | perceptual mapping | perceptual mapping | cluster | cluster | statistics | statisticsLicense

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This advanced course in anthropology engages closely with discussions and debates about ethnographic research, ethics, and representation. This advanced course in anthropology engages closely with discussions and debates about ethnographic research, ethics, and representation.Subjects

fieldwork | fieldwork | anthropology | anthropology | ethnography | ethnography | culture | culture | theory | theory | data analysis | data analysis | research design | research design | interviewing | interviewing | method | method | student work | student work | military anthropology | military anthropology | controversies | controversiesLicense

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htmSite sourced from

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This team-taught multidisciplinary course provides information relevant to the conduct and interpretation of human brain mapping studies. It begins with in-depth coverage of the physics of image formation, mechanisms of image contrast, and the physiological basis for image signals. Parenchymal and cerebrovascular neuroanatomy and application of sophisticated structural analysis algorithms for segmentation and registration of functional data are discussed. Additional topics include: fMRI experimental design including block design, event related and exploratory data analysis methods, and building and applying statistical models for fMRI data; and human subject issues including informed consent, institutional review board requirements and safety in the high field environment. Additional Facul This team-taught multidisciplinary course provides information relevant to the conduct and interpretation of human brain mapping studies. It begins with in-depth coverage of the physics of image formation, mechanisms of image contrast, and the physiological basis for image signals. Parenchymal and cerebrovascular neuroanatomy and application of sophisticated structural analysis algorithms for segmentation and registration of functional data are discussed. Additional topics include: fMRI experimental design including block design, event related and exploratory data analysis methods, and building and applying statistical models for fMRI data; and human subject issues including informed consent, institutional review board requirements and safety in the high field environment. Additional FaculSubjects

medical imaging | medical imaging | medical lab | medical lab | medical technology | medical technology | magnetic resonance imaging | magnetic resonance imaging | MRI | MRI | fMRI | fMRI | signal processing | signal processing | human brain mapping | human brain mapping | function | function | image formation physics | image formation physics | metabolism | metabolism | psychology | psychology | physiology | physiology | image signals | image signals | image processing | image processing | parenchymal | parenchymal | cerebrovascular neuroanatomy | cerebrovascular neuroanatomy | neurology | neurology | functional data analysis | functional data analysis | experimental design | experimental design | statistical models | statistical models | human subjects | human subjects | informed consent | informed consent | institutional review board requirements | institutional review board requirements | safety | safety | medical | medical | brain scan | brain scan | brain imaging | brain imaging | DTI | DTI | vision | visionLicense

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htmSite sourced from

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This course is a project-based introduction to manipulating and characterizing cells and biological molecules using microfabricated tools. It is designed for first year undergraduate students. In the first half of the term, students perform laboratory exercises designed to introduce (1) the design, manufacture, and use of microfluidic channels, (2) techniques for sorting and manipulating cells and biomolecules, and (3) making quantitative measurements using optical detection and fluorescent labeling. In the second half of the term, students work in small groups to design and test a microfluidic device to solve a real-world problem of their choosing. Includes exercises in written and oral communication and team building. This course is a project-based introduction to manipulating and characterizing cells and biological molecules using microfabricated tools. It is designed for first year undergraduate students. In the first half of the term, students perform laboratory exercises designed to introduce (1) the design, manufacture, and use of microfluidic channels, (2) techniques for sorting and manipulating cells and biomolecules, and (3) making quantitative measurements using optical detection and fluorescent labeling. In the second half of the term, students work in small groups to design and test a microfluidic device to solve a real-world problem of their choosing. Includes exercises in written and oral communication and team building.Subjects

HST.410 | HST.410 | 6.07 | 6.07 | cell manipulation | cell manipulation | microchips | microchips | lithography | lithography | rapid prototyping | rapid prototyping | optical imaging of cells | optical imaging of cells | cell sorting | cell sorting | microfluidics | microfluidics | osmosis | osmosis | diffusion | diffusion | microfabrication | microfabrication | models of diffusion | models of diffusion | laminar flow | laminar flow | MATLAB data analysis | MATLAB data analysis | cell traps | cell traps | experimental design | experimental design | cytometry techniques | cytometry techniques | computer simulation of neural behavior | computer simulation of neural behavior | casting PDMS | casting PDMS | coulter counter | coulter counter | plasma bonding | plasma bondingLicense

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htmSite sourced from

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This course is an introduction to data cleaning, analysis and visualization. We will teach the basics of data analysis through concrete examples. You will learn how to take raw data, extract meaningful information, use statistical tools, and make visualizations. This was offered as a non-credit course during the Independent Activities Period (IAP), which is a special 4-week term at MIT that runs from the first week of January until the end of the month. This course is an introduction to data cleaning, analysis and visualization. We will teach the basics of data analysis through concrete examples. You will learn how to take raw data, extract meaningful information, use statistical tools, and make visualizations. This was offered as a non-credit course during the Independent Activities Period (IAP), which is a special 4-week term at MIT that runs from the first week of January until the end of the month.Subjects

data analysis | data analysis | data cleaning | data cleaning | visualization | visualization | statistics | statistics | hypothesis testing | hypothesis testing | regression | regression | text processing | text processing | large datasets | large datasets | Hadoop | Hadoop | MapReduce | MapReduceLicense

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htmSite sourced from

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This course focuses on a number of qualitative social science methods that can be productively used in media studies research including interviewing, participant observation, focus groups, cultural probes, visual sociology, and ethnography. The emphasis will primarily be on understanding and learning concrete techniques that can be evaluated for their usefulness in any given project and utilized as needed. Data organization and analysis will be addressed. Several advanced critical thematics will also be covered, including ethics, reciprocity, "studying up," and risk. The course will be taught via a combination of lectures, class discussions, group exercises, and assignments. This course requires a willingness to work hands-on with learning various social science methods and a commitment This course focuses on a number of qualitative social science methods that can be productively used in media studies research including interviewing, participant observation, focus groups, cultural probes, visual sociology, and ethnography. The emphasis will primarily be on understanding and learning concrete techniques that can be evaluated for their usefulness in any given project and utilized as needed. Data organization and analysis will be addressed. Several advanced critical thematics will also be covered, including ethics, reciprocity, "studying up," and risk. The course will be taught via a combination of lectures, class discussions, group exercises, and assignments. This course requires a willingness to work hands-on with learning various social science methods and a commitmentSubjects

qualitative social science methods | qualitative social science methods | media studies | media studies | interview | interview | participants | participants | observation | observation | focus groups | focus groups | cultural probes | cultural probes | sociology | sociology | ethnography | ethnography | data | data | data organization | data organization | data analysis | data analysis | reciprocity | reciprocity | ethics | ethics | studying up | studying up | risk | risk | social science | social scienceLicense

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htmSite sourced from

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See all metadataTALAT Lecture 2401: Fatigue Behaviour and Analysis

Description

This lecture explains why, when and where fatigue problems may arise and the special significance to aluminium as structural material; it helps to understand the effects of material and loading parameters on fatigue; to appreciate the statistical nature of fatigue and its importance in data analysis, evaluation and use; it shows how to estimate fatigue life under service conditions of time-dependent, variable amplitude loading; how to estimate stresses acting in notches and welds with conceptual approaches other than nominal stress; it provides qualitative and quantitative information on the classification of welded details and allow for more sophisticated design procedures. Background in materials engineering, design and fatigue is required.Subjects

aluminium | aluminum | european aluminium association | EAA | Training in Aluminium Application Technologies | training | metallurgy | technology | lecture | design | fatigue | fatigue cracks | susceptibility | cyclic loading | crack growth | crack propagation rate | endurance limit | predictive theories | damage accumulation theories | Manson-Coffin law | crack growth laws | ideal cumulative damage theory | fatigue data analysis | middle-cycle fatigue range | high-cycle fatigue range | fatigue diagrams | linear P-S-N curves | non-linear P-S-N curves | service behaviour | time dependent loads | load spectrum | cycle counting | rain-flow cycle counting method | service behaviour fatigue test | analytical life estimation | damage accumulation | Palmgren-Miner linear damage accumulation hypothesis | strain | fatigue life | notch theory | strain-life diagram | weld imperfections | static strength | fatigue strength | cracks | porosity | inclusions | oxides | lack of penetration | weld shape | lack of fusion | geometric misalignment | arc strike | spatter | post-weld mechanical imperfections | corematerials | ukoerLicense

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See all metadataTALAT Lecture 2402: Design Recommendations for fatigue loaded structures

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This lecture presents calculation of design stresses for variable stress ratios in practice, explanation on the background of design recommendations; it demonstrates the concept of partial safety factors and supply appropriate background information for aluminium; it enables the designer to evaluate service behavior of structural details on a more sophisticated level applying the same principles as in current design recommendations; it provides understanding of the fatigue design procedure according to current recommendations. Background knowledge in engineering, materials and fatigue as well as some knowledge in statistics is required.Subjects

aluminium | aluminum | european aluminium association | EAA | Training in Aluminium Application Technologies | training | metallurgy | technology | lecture | design | fatigue | R-ratio effect | factor f (R) | ERAAS | residual stress effects | safety | reliability | partial safety factor | fatigue design curves | component testing | classification | design curves | S-N slope | steel codes | british standard 8118: 1991 | data analysis | Gusset plate | tubular element | welded on edge | loading | fatigue assessment | TUM-ALFABET software | corematerials | ukoerLicense

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