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5.62 Physical Chemistry II (MIT) 5.62 Physical Chemistry II (MIT)

Description

This subject deals primarily with elementary statistical mechanics, transport properties, kinetic theory, solid state, reaction rate theory, and chemical reaction dynamics.AcknowledgementsThe lecture note materials for this course include contributions from Professor Sylvia T. Ceyer. The Staff for this course would like to acknowledge that these course materials include contributions from past instructors, textbooks, and other members of the MIT Chemistry Department affiliated with course #5.62. Since the following works have evolved over a period of many years, no single source can be attributed. This subject deals primarily with elementary statistical mechanics, transport properties, kinetic theory, solid state, reaction rate theory, and chemical reaction dynamics.AcknowledgementsThe lecture note materials for this course include contributions from Professor Sylvia T. Ceyer. The Staff for this course would like to acknowledge that these course materials include contributions from past instructors, textbooks, and other members of the MIT Chemistry Department affiliated with course #5.62. Since the following works have evolved over a period of many years, no single source can be attributed.Subjects

physical chemistry | physical chemistry | partition functions | partition functions | atomic degrees of freedom | atomic degrees of freedom | molecular degrees of freedom | molecular degrees of freedom | chemical equilibrium | chemical equilibrium | thermodynamics | thermodynamics | intermolecular potentials | intermolecular potentials | equations of state | equations of state | solid state chemistry | solid state chemistry | einstein and debye solids | einstein and debye solids | kinetic theory | kinetic theory | rate theory | rate theory | chemical kinetics | chemical kinetics | transition state theory | transition state theory | RRKM theory | RRKM theory | collision theory | collision theory | equipartition | equipartition | fermi-dirac statistics | fermi-dirac statistics | boltzmann statistics | boltzmann statistics | bose-einstein statistics | bose-einstein statistics | statistical mechanics | statistical mechanicsLicense

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 metadata5.62 Physical Chemistry II (MIT) 5.62 Physical Chemistry II (MIT)

Description

This course covers elementary statistical mechanics, transport properties, kinetic theory, solid state, reaction rate theory, and chemical reaction dynamics. Acknowledgements The staff for this course would like to acknowledge that these course materials include contributions from past instructors, textbooks, and other members of the MIT Chemistry Department affiliated with course #5.62. Since the following works have evolved over a period of many years, no single source can be attributed. This course covers elementary statistical mechanics, transport properties, kinetic theory, solid state, reaction rate theory, and chemical reaction dynamics. Acknowledgements The staff for this course would like to acknowledge that these course materials include contributions from past instructors, textbooks, and other members of the MIT Chemistry Department affiliated with course #5.62. Since the following works have evolved over a period of many years, no single source can be attributed.Subjects

physical chemistry | physical chemistry | partition functions | partition functions | atomic degrees of freedom | atomic degrees of freedom | molecular degrees of freedom | molecular degrees of freedom | chemical equilibrium | chemical equilibrium | thermodynamics | thermodynamics | intermolecular potentials | intermolecular potentials | equations of state | equations of state | solid state chemistry | solid state chemistry | einstein and debye solids | einstein and debye solids | kinetic theory | kinetic theory | rate theory | rate theory | chemical kinetics | chemical kinetics | transition state theory | transition state theory | RRKM theory | RRKM theory | collision theory | collision theory | equipartition | equipartition | fermi-dirac statistics | fermi-dirac statistics | boltzmann statistics | boltzmann statistics | bose-einstein statistics | bose-einstein statistics | statistical mechanics | statistical mechanicsLicense

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 metadata18.443 Statistics for Applications (MIT) 18.443 Statistics for Applications (MIT)

Description

This course is a broad treatment of statistics, concentrating on specific statistical techniques used in science and industry. Topics include: hypothesis testing and estimation, confidence intervals, chi-square tests, nonparametric statistics, analysis of variance, regression, correlation, decision theory, and Bayesian statistics. Note: Please see the syllabus for a description of the different versions of 18.443 taught at MIT. This course is a broad treatment of statistics, concentrating on specific statistical techniques used in science and industry. Topics include: hypothesis testing and estimation, confidence intervals, chi-square tests, nonparametric statistics, analysis of variance, regression, correlation, decision theory, and Bayesian statistics. Note: Please see the syllabus for a description of the different versions of 18.443 taught at MIT.Subjects

hypothesis testing | hypothesis testing | hypothesis estimation | hypothesis estimation | confidence intervals | confidence intervals | chi-square tests | chi-square tests | nonparametric statistics | nonparametric statistics | analysis of variance | analysis of variance | regression | regression | correlation | correlation | decision theory | decision theory | Bayesian statistics | Bayesian statisticsLicense

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 metadataStatistical Methods for Planners I (MIT) Statistical Methods for Planners I (MIT)

Description

This course develops logical, empirically based arguments using statistical techniques and analytic methods. Elementary statistics, probability, and other types of quantitative reasoning useful for description, estimation, comparison, and explanation are covered. Emphasis is on the use and limitations of analytical techniques in planning practice. This course develops logical, empirically based arguments using statistical techniques and analytic methods. Elementary statistics, probability, and other types of quantitative reasoning useful for description, estimation, comparison, and explanation are covered. Emphasis is on the use and limitations of analytical techniques in planning practice.Subjects

statistics | statistics | statistical methods | statistical methods | quantitative reasoning | quantitative reasoning | variability | variability | numeracy | numeracy | measurement | measurement | stata | stata | logic | logic | probability | probability | inferential statistics | inferential statistics | regression | regression | census | census | bivariate | bivariate | multivariate | multivariate | normal curve | normal curve | research design | research design | decision tree | decision tree | utility | utility | planning | planning | distribution | distribution | city planning | city planning | scatterplot | scatterplotLicense

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 metadata18.443 Statistics for Applications (MIT) 18.443 Statistics for Applications (MIT)

Description

This course provides a broad treatment of statistics, concentrating on specific statistical techniques used in science and industry. The course topics include hypothesis testing and estimation. It also includes confidence intervals, chi-square tests, nonparametric statistics, analysis of variance, regression, and correlation. This course provides a broad treatment of statistics, concentrating on specific statistical techniques used in science and industry. The course topics include hypothesis testing and estimation. It also includes confidence intervals, chi-square tests, nonparametric statistics, analysis of variance, regression, and correlation.Subjects

hypothesis testing and estimation; confidence intervals; chi-square tests; nonparametric statistics; analysis of variance; regression; correlation | hypothesis testing and estimation; confidence intervals; chi-square tests; nonparametric statistics; analysis of variance; regression; correlation | hypothesis testing and estimation | hypothesis testing and estimation | confidence intervals | confidence intervals | chi-square tests | chi-square tests | nonparametric statistics | nonparametric statistics | analysis of variance | analysis of variance | regression | regression | correlation | correlationLicense

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 metadata18.443 Statistics for Applications (MIT) 18.443 Statistics for Applications (MIT)

Description

This course is a broad treatment of statistics, concentrating on specific statistical techniques used in science and industry. Topics include: hypothesis testing and estimation, confidence intervals, chi-square tests, nonparametric statistics, analysis of variance, regression, correlation, decision theory, and Bayesian statistics. This course is a broad treatment of statistics, concentrating on specific statistical techniques used in science and industry. Topics include: hypothesis testing and estimation, confidence intervals, chi-square tests, nonparametric statistics, analysis of variance, regression, correlation, decision theory, and Bayesian statistics.Subjects

hypothesis testing | hypothesis testing | hypothesis estimation | hypothesis estimation | confidence intervals | confidence intervals | chi-square tests | chi-square tests | nonparametric statistics | nonparametric statistics | analysis of variance | analysis of variance | regression | regression | correlation | correlation | decision theory | decision theory | Bayesian statistics | Bayesian statisticsLicense

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 metadata18.443 Statistics for Applications (MIT) 18.443 Statistics for Applications (MIT)

Description

This course is a broad treatment of statistics, concentrating on specific statistical techniques used in science and industry. Topics include: hypothesis testing and estimation, confidence intervals, chi-square tests, nonparametric statistics, analysis of variance, regression, correlation, decision theory, and Bayesian statistics. Note: Please see the syllabus for a description of the different versions of 18.443 taught at MIT. This course is a broad treatment of statistics, concentrating on specific statistical techniques used in science and industry. Topics include: hypothesis testing and estimation, confidence intervals, chi-square tests, nonparametric statistics, analysis of variance, regression, correlation, decision theory, and Bayesian statistics. Note: Please see the syllabus for a description of the different versions of 18.443 taught at MIT.Subjects

hypothesis testing | hypothesis testing | hypothesis estimation | hypothesis estimation | confidence intervals | confidence intervals | chi-square tests | chi-square tests | nonparametric statistics | nonparametric statistics | analysis of variance | analysis of variance | regression | regression | correlation | correlation | decision theory | decision theory | Bayesian statistics | Bayesian statisticsLicense

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 introduces statistical tools and techniques that are routinely used by modern statisticians for a wide variety of applications. This free course may be completed online at any time. See course site for detailed overview and learning outcomes. (Mathematics 251)Subjects

statistics | regression | correlation | experimental design | parametric statistics | non-parametric statistics | Computer science | I100License

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See all metadata5.62 Physical Chemistry II (MIT)

Description

This subject deals primarily with elementary statistical mechanics, transport properties, kinetic theory, solid state, reaction rate theory, and chemical reaction dynamics.AcknowledgementsThe lecture note materials for this course include contributions from Professor Sylvia T. Ceyer. The Staff for this course would like to acknowledge that these course materials include contributions from past instructors, textbooks, and other members of the MIT Chemistry Department affiliated with course #5.62. Since the following works have evolved over a period of many years, no single source can be attributed.Subjects

physical chemistry | partition functions | atomic degrees of freedom | molecular degrees of freedom | chemical equilibrium | thermodynamics | intermolecular potentials | equations of state | solid state chemistry | einstein and debye solids | kinetic theory | rate theory | chemical kinetics | transition state theory | RRKM theory | collision theory | equipartition | fermi-dirac statistics | boltzmann statistics | bose-einstein statistics | statistical mechanicsLicense

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 https://ocw.mit.edu/terms/index.htmSite sourced from

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See all metadata5.62 Physical Chemistry II (MIT)

Description

This course covers elementary statistical mechanics, transport properties, kinetic theory, solid state, reaction rate theory, and chemical reaction dynamics. Acknowledgements The staff for this course would like to acknowledge that these course materials include contributions from past instructors, textbooks, and other members of the MIT Chemistry Department affiliated with course #5.62. Since the following works have evolved over a period of many years, no single source can be attributed.Subjects

physical chemistry | partition functions | atomic degrees of freedom | molecular degrees of freedom | chemical equilibrium | thermodynamics | intermolecular potentials | equations of state | solid state chemistry | einstein and debye solids | kinetic theory | rate theory | chemical kinetics | transition state theory | RRKM theory | collision theory | equipartition | fermi-dirac statistics | boltzmann statistics | bose-einstein statistics | statistical mechanicsLicense

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 https://ocw.mit.edu/terms/index.htmSite sourced from

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See all metadata2.717J Optical Engineering (MIT) 2.717J Optical Engineering (MIT)

Description

This course concerns the theory and practice of optical methods in engineering and system design, with an emphasis on diffraction, statistical optics, holography, and imaging. It provides the engineering methodology skills necessary to incorporate optical components in systems serving diverse areas such as precision engineering and metrology, bio-imaging, and computing (sensors, data storage, communication in multi-processor systems). Experimental demonstrations and a design project are included. This course concerns the theory and practice of optical methods in engineering and system design, with an emphasis on diffraction, statistical optics, holography, and imaging. It provides the engineering methodology skills necessary to incorporate optical components in systems serving diverse areas such as precision engineering and metrology, bio-imaging, and computing (sensors, data storage, communication in multi-processor systems). Experimental demonstrations and a design project are included.Subjects

optical methods in engineering and system design | optical methods in engineering and system design | diffraction | statistical optics | holography | and imaging | diffraction | statistical optics | holography | and imaging | Statistical Optics | Inverse Problems (i.e. theory of imaging) | Statistical Optics | Inverse Problems (i.e. theory of imaging) | applications in precision engineering and metrology | bio-imaging | and computing (sensors | data storage | communication in multi-processor systems) | applications in precision engineering and metrology | bio-imaging | and computing (sensors | data storage | communication in multi-processor systems) | Fourier optics | Fourier optics | probability | probability | stochastic processes | stochastic processes | light statistics | light statistics | theory of light coherence | theory of light coherence | van Cittert-Zernicke Theorem | van Cittert-Zernicke Theorem | statistical optics applications | statistical optics applications | inverse problems | inverse problems | information-theoretic views | information-theoretic views | information theory | information theory | 2.717 | 2.717 | MAS.857 | MAS.857License

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 metadata14.661 Labor Economics I (MIT) 14.661 Labor Economics I (MIT)

Description

Neoclassical analysis of the labor market and its institutions. A systematic development of the theory of labor supply, labor demand, and human capital. Topics discussed also include wage and employment determination, turnover, search, immigration, unemployment, equalizing differences, and institutions in the labor market. There is particular emphasis on the interaction of theoretical and empirical modeling and the development of independent research interests. Neoclassical analysis of the labor market and its institutions. A systematic development of the theory of labor supply, labor demand, and human capital. Topics discussed also include wage and employment determination, turnover, search, immigration, unemployment, equalizing differences, and institutions in the labor market. There is particular emphasis on the interaction of theoretical and empirical modeling and the development of independent research interests.Subjects

labor economics | public policy | schooling | learning | matching | experience | wages | minimum wage | college | investment | training | firms | corporations | labor | unions | panel data | neoclassical model | turnover models | turnover | economics | labor economics | public policy | schooling | learning | matching | experience | wages | minimum wage | college | investment | training | firms | corporations | labor | unions | panel data | neoclassical model | turnover models | turnover | economics | labor | labor | market | market | statistics | statistics | theory | theory | neoclassical | neoclassical | supply | supply | model | model | life-cycle | life-cycle | demand | demand | wages | wages | immigration | immigration | human capital | human capital | econometrics | econometrics | liquidity | liquidity | constraints | constraints | mobility | mobility | incentives | incentives | organization | organization | moral hazard | moral hazard | insurance | insurance | investments | investments | efficiency | efficiency | unemployment | unemployment | search | search | jobs | jobs | training | training | capital | capital | firm | firm | technology | technology | skills | skills | risk | risk | signaling | signaling | discrimination | discrimination | self-selection | self-selection | learning | learning | natives | nativesLicense

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 class introduces elementary programming concepts including variable types, data structures, and flow control. After an introduction to linear algebra and probability, it covers numerical methods relevant to mechanical engineering, including approximation (interpolation, least squares and statistical regression), integration, solution of linear and nonlinear equations, ordinary differential equations, and deterministic and probabilistic approaches. Examples are drawn from mechanical engineering disciplines, in particular from robotics, dynamics, and structural analysis. Assignments require MATLAB® programming. Includes audio/video content: AV special element video. This class introduces elementary programming concepts including variable types, data structures, and flow control. After an introduction to linear algebra and probability, it covers numerical methods relevant to mechanical engineering, including approximation (interpolation, least squares and statistical regression), integration, solution of linear and nonlinear equations, ordinary differential equations, and deterministic and probabilistic approaches. Examples are drawn from mechanical engineering disciplines, in particular from robotics, dynamics, and structural analysis. Assignments require MATLAB® programming.Subjects

MATLAB | MATLAB | numerical analysis | numerical analysis | programming | programming | physical modeling | physical modeling | calculus | calculus | linear algebra | linear algebra | Monte Carlo Method | Monte Carlo Method | differential equations | differential equations | nonlinear systems | nonlinear systems | variable types | variable types | data structure | data structure | flow control | flow control | probability | probability | statistics | statistics | robotics | roboticsLicense

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 metadata18.443 Statistics for Applications (MIT)

Description

This course is a broad treatment of statistics, concentrating on specific statistical techniques used in science and industry. Topics include: hypothesis testing and estimation, confidence intervals, chi-square tests, nonparametric statistics, analysis of variance, regression, correlation, decision theory, and Bayesian statistics.Subjects

hypothesis testing | hypothesis estimation | confidence intervals | chi-square tests | nonparametric statistics | analysis of variance | regression | correlation | decision theory | Bayesian statisticsLicense

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 https://ocw.mit.edu/terms/index.htmSite sourced from

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This unit looks at a wide variety of ways of comparing prices and the construction of a price index. You will also look at the Retail Price Index (RPI) and the Consumer Price Index (CPI), indices used by the UK Government to calculate the percentage by which prices in general have risen over any given period. You wil also look at the important statistical and mathematical ideas that contribute to the construction of a price index.Subjects

mathematics and statistics | communicating_maths | mathematical | mathematics | prices | rpi | statistical | statistics | Education | X000License

Attribution-Noncommercial-Share Alike 2.0 UK: England & Wales Attribution-Noncommercial-Share Alike 2.0 UK: England & Wales http://creativecommons.org/licenses/by-nc-sa/2.0/uk/ http://creativecommons.org/licenses/by-nc-sa/2.0/uk/Site sourced from

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This is a seminar based on research literature. Papers covered are selected to illustrate important problems and approaches in the field of computational and systems biology, and provide students a framework from which to evaluate new developments. The MIT Initiative in Computational and Systems Biology (CSBi) is a campus-wide research and education program that links biology, engineering, and computer science in a multidisciplinary approach to the systematic analysis and modeling of complex biological phenomena. This course is one of a series of core subjects offered through the CSB PhD program, for students with an interest in interdisciplinary training and research in the area of computational and systems biology. Acknowledgments In addition to the staff listed on this page, the followi This is a seminar based on research literature. Papers covered are selected to illustrate important problems and approaches in the field of computational and systems biology, and provide students a framework from which to evaluate new developments. The MIT Initiative in Computational and Systems Biology (CSBi) is a campus-wide research and education program that links biology, engineering, and computer science in a multidisciplinary approach to the systematic analysis and modeling of complex biological phenomena. This course is one of a series of core subjects offered through the CSB PhD program, for students with an interest in interdisciplinary training and research in the area of computational and systems biology. Acknowledgments In addition to the staff listed on this page, the followiSubjects

computational | computational | systems | systems | biology | biology | seminar | seminar | literature review | literature review | statistics | statistics | developmental | developmental | biochemistry | biochemistry | genetics | genetics | physics | physics | genomics | genomics | signal transduction | signal transduction | regulation | regulation | medicine | medicine | kinetics | kinetics | protein structure | protein structure | devices | devices | synthesis | synthesis | networks | networks | mapping | mappingLicense

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 metadata14.381 Statistical Method in Economics (MIT) 14.381 Statistical Method in Economics (MIT)

Description

This course is divided into two sections, Part I and Part II. Part I, found here, provides an introduction to statistical theory. A brief review of probability will be given mainly as background material, however, it is assumed to be known. Topics include normal distribution, limit theorems, Bayesian concepts, and testing, among others. Part II prepares students for the remainder of the econometrics sequence and and can be found by visiting 14.381 Fall 2006. This course is divided into two sections, Part I and Part II. Part I, found here, provides an introduction to statistical theory. A brief review of probability will be given mainly as background material, however, it is assumed to be known. Topics include normal distribution, limit theorems, Bayesian concepts, and testing, among others. Part II prepares students for the remainder of the econometrics sequence and and can be found by visiting 14.381 Fall 2006.Subjects

economics | economics | statistics | statistics | sample | sample | population | population | convergence | convergence | limits | limits | method | method | testing | testing | confidence sets | confidence sets | Bayesian | BayesianLicense

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 metadata5.72 Statistical Mechanics (MIT) 5.72 Statistical Mechanics (MIT)

Description

This course discusses the principles and methods of statistical mechanics. Topics covered include classical and quantum statistics, grand ensembles, fluctuations, molecular distribution functions, other concepts in equilibrium statistical mechanics, and topics in thermodynamics and statistical mechanics of irreversible processes. This course discusses the principles and methods of statistical mechanics. Topics covered include classical and quantum statistics, grand ensembles, fluctuations, molecular distribution functions, other concepts in equilibrium statistical mechanics, and topics in thermodynamics and statistical mechanics of irreversible processes.Subjects

statistical mechanics | statistical mechanics | quantum | quantum | statistics | statistics | atoms | atoms | materials | materials | master equations | master equations | random walk | random walk | langevin | langevin | fokker | fokker | planck | planck | probability theory | probability theory | bloch-redfield | bloch-redfield | navier-stokes | navier-stokes | hydrodynamic | hydrodynamic | scattering | scattering | projection operator | projection operator | thermodynamics | thermodynamicsLicense

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 covers interpretations of the concept of probability. Topics include basic probability rules; random variables and distribution functions; functions of random variables; and applications to quality control and the reliability assessment of mechanical/electrical components, as well as simple structures and redundant systems. The course also considers elements of statistics; Bayesian methods in engineering; methods for reliability and risk assessment of complex systems (event-tree and fault-tree analysis, common-cause failures, human reliability models); uncertainty propagation in complex systems (Monte Carlo methods, Latin Hypercube Sampling); and an introduction to Markov models. Examples and applications are drawn from nuclear and other industries, waste repositories, and mech This course covers interpretations of the concept of probability. Topics include basic probability rules; random variables and distribution functions; functions of random variables; and applications to quality control and the reliability assessment of mechanical/electrical components, as well as simple structures and redundant systems. The course also considers elements of statistics; Bayesian methods in engineering; methods for reliability and risk assessment of complex systems (event-tree and fault-tree analysis, common-cause failures, human reliability models); uncertainty propagation in complex systems (Monte Carlo methods, Latin Hypercube Sampling); and an introduction to Markov models. Examples and applications are drawn from nuclear and other industries, waste repositories, and mechSubjects

risk | risk | uncertainty | uncertainty | nuclear accident | nuclear accident | disaster | disaster | meltdown | meltdown | probability | probability | risk assessment | risk assessment | PRA | PRA | probabalistic risk assessment | probabalistic risk assessment | NUREG-1150 | NUREG-1150 | WASH-1400 | WASH-1400 | failure | failure | applied probability | applied probability | applied statistics | applied statistics | system performance | system performance | MTBF | MTBF | decision | decision | hazard | hazard | fault tree analysis | fault tree analysis | event tree analysis | event tree 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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See all metadataNumeracy for Professional Purposes (3/10): Basic Descriptive Statistics: Introduction

Description

Basic Descriptive Statistics: IntroductionSubjects

inferential statistics | univariate | measures of distribution | measures of central tendency | measures of dispersion | ukoer | lfwoer | learning from woerk | uopcpdrm | continuous professional development | cpd | work-based learning | wbl | descriptive statistics | Social studies | L000License

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See all metadata8.05 Quantum Physics II (MIT) 8.05 Quantum Physics II (MIT)

Description

This course, along with the next course in this sequence (8.06, Quantum Physics III) in a two-course sequence covering quantum physics with applications drawn from modern physics. General formalism of quantum mechanics: states, operators, Dirac notation, representations, measurement theory. Harmonic oscillator: operator algebra, states. Quantum mechanics in three-dimensions: central potentials and the radial equation, bound and scattering states, qualitative analysis of wavefunctions. Angular momentum: operators, commutator algebra, eigenvalues and eigenstates, spherical harmonics. Spin: Stern-Gerlach devices and measurements, nuclear magnetic resonance, spin and statistics. Addition of angular momentum: Clebsch-Gordan series and coefficients, spin systems, and allotropic forms of hydrogen This course, along with the next course in this sequence (8.06, Quantum Physics III) in a two-course sequence covering quantum physics with applications drawn from modern physics. General formalism of quantum mechanics: states, operators, Dirac notation, representations, measurement theory. Harmonic oscillator: operator algebra, states. Quantum mechanics in three-dimensions: central potentials and the radial equation, bound and scattering states, qualitative analysis of wavefunctions. Angular momentum: operators, commutator algebra, eigenvalues and eigenstates, spherical harmonics. Spin: Stern-Gerlach devices and measurements, nuclear magnetic resonance, spin and statistics. Addition of angular momentum: Clebsch-Gordan series and coefficients, spin systems, and allotropic forms of hydrogenSubjects

General formalism of quantum mechanics: states | General formalism of quantum mechanics: states | operators | operators | Dirac notation | Dirac notation | representations | representations | measurement theory | measurement theory | Harmonic oscillator: operator algebra | Harmonic oscillator: operator algebra | states | states | Quantum mechanics in three-dimensions: central potentials and the radial equation | Quantum mechanics in three-dimensions: central potentials and the radial equation | bound and scattering states | bound and scattering states | qualitative analysis of wavefunctions | qualitative analysis of wavefunctions | Angular momentum: operators | Angular momentum: operators | commutator algebra | commutator algebra | eigenvalues and eigenstates | eigenvalues and eigenstates | spherical harmonics | spherical harmonics | Spin: Stern-Gerlach devices and measurements | Spin: Stern-Gerlach devices and measurements | nuclear magnetic resonance | nuclear magnetic resonance | spin and statistics | spin and statistics | Addition of angular momentum: Clebsch-Gordan series and coefficients | Addition of angular momentum: Clebsch-Gordan series and coefficients | spin systems | spin systems | allotropic forms of hydrogen | allotropic forms of hydrogen | Angular momentum | Angular momentum | Harmonic oscillator | Harmonic oscillator | operator algebra | operator algebra | Spin | Spin | Stern-Gerlach devices and measurements | Stern-Gerlach devices and measurements | central potentials and the radial equation | central potentials and the radial equation | Clebsch-Gordan series and coefficients | Clebsch-Gordan series and coefficients | quantum physics | quantum physicsLicense

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 will provide a solid foundation in probability and statistics for economists and other social scientists. We will emphasize topics needed for further study of econometrics and provide basic preparation for 14.32. Topics include elements of probability theory, sampling theory, statistical estimation, and hypothesis testing. This course will provide a solid foundation in probability and statistics for economists and other social scientists. We will emphasize topics needed for further study of econometrics and provide basic preparation for 14.32. Topics include elements of probability theory, sampling theory, statistical estimation, and hypothesis testing.Subjects

statistics | statistics | economic applications | economic applications | probability theory | probability theory | sampling theory | sampling theory | statistical estimation | statistical estimation | regression analysis | regression analysis | hypothesis testing | hypothesis testing | Elementary econometrics | Elementary econometrics | statistical tools | statistical tools | economic data | economic data | economics | economics | statistical | statistical | probability distribution function | probability distribution function | cumulative distribution function | cumulative distribution function | normal | normal | Student's t | Student's t | chi-squared | chi-squared | central limit theorem | central limit theorem | law of large numbers | law of large numbers | Bayes theorem | Bayes theoremLicense

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 metadata17.869 Political Science Scope and Methods (MIT) 17.869 Political Science Scope and Methods (MIT)

Description

This course is designed to provide an introduction to a variety of empirical research methods used by political scientists. The primary aims of the course are to make you a more sophisticated consumer of diverse empirical research and to allow you to conduct sophisticated independent work in your junior and senior years. This is not a course in data analysis. Rather, it is a course on how to approach political science research. This course is designed to provide an introduction to a variety of empirical research methods used by political scientists. The primary aims of the course are to make you a more sophisticated consumer of diverse empirical research and to allow you to conduct sophisticated independent work in your junior and senior years. This is not a course in data analysis. Rather, it is a course on how to approach political science research.Subjects

political science | political science | empirical research | empirical research | scientific method | scientific method | research design | research design | models | models | samping | samping | statistical analysis | statistical analysis | measurement | measurement | ethics | ethics | empirical | empirical | research | research | scientific | scientific | methods | methods | statistics | statistics | statistical | statistical | analysis | analysis | political | political | politics | politics | science | science | design | design | sampling | sampling | theoretical | theoretical | observation | observation | data | data | case studies | case studies | cases | cases | empirical research methods | empirical research methods | political scientists | political scientists | empirical analysis | empirical analysis | theoretical analysis | theoretical analysis | research projects | research projects | department faculty | department faculty | inference | inference | writing | writing | revision | revision | oral presentations | oral presentations | experimental method | experimental method | theories | theories | political implications | political implicationsLicense

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 metadata24.963 Linguistic Phonetics (MIT) 24.963 Linguistic Phonetics (MIT)

Description

Includes audio/video content: AV special element audio. This course is a study of speech sounds: how we produce and perceive them and their acoustic properties. It explores the influence of the production and perception systems on phonological patterns and sound change. Acoustic analysis and experimental techniques are also discussed. Includes audio/video content: AV special element audio. This course is a study of speech sounds: how we produce and perceive them and their acoustic properties. It explores the influence of the production and perception systems on phonological patterns and sound change. Acoustic analysis and experimental techniques are also discussed.Subjects

phonetics | phonetics | acoustics | acoustics | audition | audition | A/D conversion | A/D conversion | grammars | grammars | source-filter theory | source-filter theory | spectral analysis | spectral analysis | adaptive dispersion | adaptive dispersion | quantal theory | quantal theory | fricatives | fricatives | stops | stops | statistics | statistics | speech perception | speech perception | sounds | sounds | nasals | nasals | laterals | laterals | coarticulation | coarticulation | speech production | speech production | timing | timing | coordination | coordination | variability | variabilityLicense

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 metadata22.058 Principles of Medical Imaging (MIT) 22.058 Principles of Medical Imaging (MIT)

Description

An introduction to the principles of tomographic imaging and its applications. It includes a series of lectures with a parallel set of recitations that provide demonstrations of basic principles. Both ionizing and non-ionizing radiation are covered, including x-ray, PET, MRI, and ultrasound. Emphasis on the physics and engineering of image formation. An introduction to the principles of tomographic imaging and its applications. It includes a series of lectures with a parallel set of recitations that provide demonstrations of basic principles. Both ionizing and non-ionizing radiation are covered, including x-ray, PET, MRI, and ultrasound. Emphasis on the physics and engineering of image formation.Subjects

general imaging principles | | general imaging principles | | linear optics | | linear optics | | ray tracing | | ray tracing | | Linear Imaging Systems | | Linear Imaging Systems | | Space Invariance | | Space Invariance | | Pin-hole camera | | Pin-hole camera | | Fourier Transformations | | Fourier Transformations | | Modulation Transfer Functions | | Modulation Transfer Functions | | Fourier convolution | | Fourier convolution | | Sampling | | Sampling | | Nyquist | | Nyquist | | counting statistics | | counting statistics | | additive noise | | additive noise | | optical imaging | | optical imaging | | Radiation types | | Radiation types | | Radiation detection | | Radiation detection | | photon detection | | photon detection | | spectra | | spectra | | attenuation | | attenuation | | Planar X-ray imaging | | Planar X-ray imaging | | Projective Imaging | | Projective Imaging | | X-ray CT | | X-ray CT | | Ultrasound | | Ultrasound | | microscopy | k-space | | microscopy | k-space | | NMR pulses | | NMR pulses | | f2-D gradient | | f2-D gradient | | spin echoes | | spin echoes | | 3-D methods of MRI | | 3-D methods of MRI | | volume localized spectroscopy | volume localized spectroscopyLicense

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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