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Readme file for Introduction to OO Programming in Java

Description

This readme file contains details of links to all the Introduction to OO Programming in Java module's material held on Jorum and information about the module as well.Subjects

ukoer | programming task guide | programming lecture | programming reading material | software design reading material | classes guide | libraries lecture | classes reading material | classes visual aid | software objects guide | graphics reading material | attributes reading material | attributes visual guide | naming conventions reading material | code reading material | java keywords reading material | variables visual guide | arithmetic reading material | java assignment | making decisions task guide | making decisions lecture | making decisions reading material | boolean expressions visual guide | repetition reading material | while loops visual guide | methods reading material | methods practical | access modifiers reading material | objects reading material | classes assignment | classes practical | child classes task guide | inheritance task guide | extending classes lecture | inheritance reading material | inheritance visual guide | inheritance practical | graphics task guide | awt reading material | graphics visual aid | awt class library reading material | event-driven programming reading material | scrollbars reading material | reflective practice visual guide | mobile phone task guide | mobile phone lecture | fixed repitition reading material | fixed repitition visual guide | mobile phone library reading material | mobile phone reading material | arrays task guide | arrays lecture | arrays reading material | arrays visual guide | creating software objects reading material | software objects visual guide | java practical | generic array list task guide | overriding methods reading material | menu and switch task guide | multi-way decisions reading material | multi-way decisions visual guide | searching task guide | searching lecture | searching reading material | software quality task guide | software quality lecture | software quality reading material | programming assignment | applet reading material | classes visual guide | object-oriented programming | object-oriented | programming | java | problem solving | java program | software design | programming languages | computers | class task guide | class reading material | class assignment | class practical | java classes | variables | attributes | arithmetic | java class | classes and arithmetic | classes | class | decisions | boolean expression | boolean expressions | repetition | methods | aggregate classes | access modifier | access modifiers | child classes | inheritance | child class | graphics | awt class library | fixed repetition | for loop | for loops | array | arrays | iteration | software object | definite iteration | generic lists | generic array list | cast | casting | overriding method | overriding methods | generic list | menu-driven program | menu-driven programs | multi-way decisions | menu and switch | search | searching | software quality | testing | software quality and testing | assessment | computers task guide | programming languages task guide | software design task guide | java program task guide | problem-solving task guide | problem solving task guide | object-oriented programming task guide | java task guide | object-oriented task guide | object oriented task guide | computers lecture | programming languages lecture | software design lecture | java program lecture | problem solving lecture | object-oriented programming lecture | java lecture | object oriented programming lecture | object-oriented lecture | computers reading material | programming languages reading material | java program reading material | problem solving reading material | object-oriented programming reading material | java reading material | object-oriented reading material | object oriented reading material | java classes task guide | variables task guide | attributes task guide | arithmetic task guide | java class task guide | classes and arithmetic task guide | classes task guide | java classes lecture | variables lecture | attributes lecture | arithmetic lecture | java class lecture | classes and arithmetic lecture | classes lecture | class lecture | java classes reading material | variables reading material | java class reading material | classes and arithmetic reading material | java classes visual aid | variables visual aid | attributes visual aid | arithmetic visual aid | java class visual aid | classes and arithmetic visual aid | class visual aid | java visual aid | object-oriented programming visual aid | programming visual aid | object-oriented visual aid | decisions task guide | boolean expression task guide | boolean expressions task guide | repetition task guide | methods task guide | decisions lecture | boolean expression lecture | boolean expressions lecture | repetition lecture | methods lecture | decisions reading material | boolean expression reading material | boolean expressions reading material | decisions visual aid | boolean expression visual aid | boolean expressions visual aid | repetition visual aid | methods visual aid | decisions practical | boolean expression practical | boolean expressions practical | repetition practical | programming practical | object oriented programming practical | object-oriented programming practical | object-oriented practical | object oriented practical | aggregate classes task guide | access modifier task guide | access modifiers task guide | aggregate classes lecture | access modifier lecture | access modifiers lecture | aggregate classes reading material | access modifier reading material | aggregate classes assignment | java classes assignment | access modifier assignment | access modifiers assignment | object oriented programming assignment | object-oriented programming assignment | object-oriented assignment | object oriented assignment | child class task guide | child classes lecture | inheritance lecture | child class lecture | child classes reading material | child class reading material | child classes visual aid | inheritance visual aid | child class visual aid | awt class library task guide | graphics lecture | awt class library lecture | awt class library visual aid | graphics assignment | awt class library assignment | fixed repetition task guide | fixed repetition lecture | fixed repetition visual aid | fixed repetition reading material | for loop task guide | for loops task guide | array task guide | iteration task guide | software object task guide | definite iteration task guide | for loop lecture | for loops lecture | array lecture | iteration lecture | software object lecture | definite iteration lecture | for loop reading material | for loops reading material | array reading material | iteration reading material | software object reading material | definite iteration reading material | for loop visual aid | for loops visual aid | array visual aid | arrays visual aid | iteration visual aid | software object visual aid | definite iteration visual aid | generic lists task guide | cast task guide | casting task guide | overriding method task guide | overriding methods task guide | generic list task guide | generic lists lecture | generic array list lecture | cast lecture | casting lecture | overriding method lecture | overriding methods lecture | generic list lecture | generic lists reading material | generic array list reading material | cast reading material | casting reading material | overriding method reading material | generic list reading material | menu-driven program task guide | menu-driven programs task guide | multi-way decisions task guide | menu-driven program lecture | menu-driven programs lecture | multi-way decisions lecture | menu and switch lecture | menu-driven program reading material | menu-driven programs reading material | menu and switch reading material | menu-driven program visual aid | menu-driven programs visual aid | multi-way decisions visual aid | menu and switch visual aid | search task guide | search lecture | search reading material | testing task guide | software quality and testing task guide | testing lecture | software quality and testing lecture | testing reading material | software quality and testing reading material | assessment reading material | assessment assignment | fixed repetition practical | jcreator guide | g622 | oo | oop | oo programming | awt | oo programming task guide | oop task guide | oo task guide | g622 task guide | oo programming lecture | oop lecture | oo lecture | g622 lecture | oo programming reading material | oop reading material | oo reading material | g622 reading material | g622 visual aid | oop visual aid | oo visual aid | oo programming visual aid | g622 practical | oo practical | oo programming practical | oop practical | g622 assignment | oo assignment | oop assignment | oo programming assignment | awt task guide | awt lecture | awt visual aid | awt assignment | Computer science | I100License

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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See all metadata18.024 Calculus with Theory II (MIT) 18.024 Calculus with Theory II (MIT)

Description

This course is a continuation of 18.014. It covers the same material as 18.02 (Calculus), but at a deeper level, emphasizing careful reasoning and understanding of proofs. There is considerable emphasis on linear algebra and vector integral calculus.Topics include: Calculus of several variables. Vector algebra in 3-space, determinants, matrices. Vector-valued functions of one variable, space motion. Scalar functions of several variables: partial differentiation, gradient, optimization techniques. Double integrals and line integrals in the plane; exact differentials and conservative fields; Green's theorem and applications, triple integrals, line and surface integrals in space, Divergence theorem, Stokes' theorem; applications. Dr. Lachowska wishes to acknowledge Andrew Brooke-Taylor This course is a continuation of 18.014. It covers the same material as 18.02 (Calculus), but at a deeper level, emphasizing careful reasoning and understanding of proofs. There is considerable emphasis on linear algebra and vector integral calculus.Topics include: Calculus of several variables. Vector algebra in 3-space, determinants, matrices. Vector-valued functions of one variable, space motion. Scalar functions of several variables: partial differentiation, gradient, optimization techniques. Double integrals and line integrals in the plane; exact differentials and conservative fields; Green's theorem and applications, triple integrals, line and surface integrals in space, Divergence theorem, Stokes' theorem; applications. Dr. Lachowska wishes to acknowledge Andrew Brooke-TaylorSubjects

linear algebra | linear algebra | vector integral calculus | vector integral calculus | Calculus of several variables | Calculus of several variables | Vector algebra in 3-space | Vector algebra in 3-space | determinants | determinants | matrices | matrices | Vector-valued functions of one variable | Vector-valued functions of one variable | space motion | space motion | Scalar functions of several variables: partial differentiation | Scalar functions of several variables: partial differentiation | gradient | gradient | optimization techniques | optimization techniques | Double integrals and line integrals in the plane | Double integrals and line integrals in the plane | exact differentials and conservative fields | exact differentials and conservative fields | Green's theorem and applications | Green's theorem and applications | triple integrals | triple integrals | line and surface integrals in space | line and surface integrals in space | Divergence theorem | Divergence theorem | Stokes' theorem | Stokes' theorem | applications | applicationsLicense

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 lectures. Welcome to 6.041/6.431, a subject on the modeling and analysis of random phenomena and processes, including the basics of statistical inference. Nowadays, there is broad consensus that the ability to think probabilistically is a fundamental component of scientific literacy. For example: The concept of statistical significance (to be touched upon at the end of this course) is considered by the Financial Times as one of "The Ten Things Everyone Should Know About Science". A recent Scientific American article argues that statistical literacy is crucial in making health-related decisions. Finally, an article in the New York Times identifies statistical data analysis as an upcoming profession, valuable everywhere, from Google and Includes audio/video content: AV lectures. Welcome to 6.041/6.431, a subject on the modeling and analysis of random phenomena and processes, including the basics of statistical inference. Nowadays, there is broad consensus that the ability to think probabilistically is a fundamental component of scientific literacy. For example: The concept of statistical significance (to be touched upon at the end of this course) is considered by the Financial Times as one of "The Ten Things Everyone Should Know About Science". A recent Scientific American article argues that statistical literacy is crucial in making health-related decisions. Finally, an article in the New York Times identifies statistical data analysis as an upcoming profession, valuable everywhere, from Google andSubjects

probability | probability | probability models | probability models | bayes rule | bayes rule | discrete random variables | discrete random variables | continuous random variables | continuous random variables | bernoulli process | bernoulli process | poisson process | poisson process | markov chains | markov chains | central limit theorem | central limit theorem | statistical inference | statistical inferenceLicense

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 metadata6.094 Introduction to MATLAB (MIT) 6.094 Introduction to MATLAB (MIT)

Description

This course provides an aggressively gentle introduction to MATLAB®. It is designed to give students fluency in MATLAB, including popular toolboxes. The course consists of interactive lectures with students doing sample MATLAB problems in real time. Problem-based MATLAB assignments are given which require significant time on MATLAB. This course is offered 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. Acknowledgements The 6.094 course materials were developed by Danilo Å Ä‡epanoviÄ‡, Sourav R. Dey, Ankit Patel, and Patrick Ho. This course provides an aggressively gentle introduction to MATLAB®. It is designed to give students fluency in MATLAB, including popular toolboxes. The course consists of interactive lectures with students doing sample MATLAB problems in real time. Problem-based MATLAB assignments are given which require significant time on MATLAB. This course is offered 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. Acknowledgements The 6.094 course materials were developed by Danilo Å Ä‡epanoviÄ‡, Sourav R. Dey, Ankit Patel, and Patrick Ho.Subjects

introduction to MATLAB | introduction to MATLAB | scripts | scripts | making variables | making variables | manipulating variables | manipulating variables | functions | functions | flow control | flow control | line plots | line plots | surface plots | surface plots | vectorization | vectorization | linear algebra | linear algebra | optimization | optimization | differential equations | differential equations | data structures | data structures | debugging | debugging | animation | animation | symbolic math | symbolic math | Simulink | Simulink | file input/output | file input/output | graphical user interfaces | graphical user interfacesLicense

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 metadata14.382 Econometrics I (MIT) 14.382 Econometrics I (MIT)

Description

This course focuses on the specification and estimation of the linear regression model. The course departs from the standard Gauss-Markov assumptions to include heteroskedasticity, serial correlation, and errors in variables. Advanced topics include generalized least squares, instrumental variables, nonlinear regression, and limited dependent variable models. Economic applications are discussed throughout the course. This course focuses on the specification and estimation of the linear regression model. The course departs from the standard Gauss-Markov assumptions to include heteroskedasticity, serial correlation, and errors in variables. Advanced topics include generalized least squares, instrumental variables, nonlinear regression, and limited dependent variable models. Economic applications are discussed throughout the course.Subjects

Economics | Economics | econometrics | econometrics | linear regression model | linear regression model | Gauss-Markov | Gauss-Markov | heteroskedasticity | heteroskedasticity | serial correlation | serial correlation | errors | errors | variables | variables | generalized least squares | generalized least squares | instrumental variables | instrumental variables | nonlinear regression | nonlinear regression | limited dependent variable models | limited dependent variable 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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See all metadata16.13 Aerodynamics of Viscous Fluids (MIT) 16.13 Aerodynamics of Viscous Fluids (MIT)

Description

The major focus of 16.13 is on boundary layers, and boundary layer theory subject to various flow assumptions, such as compressibility, turbulence, dimensionality, and heat transfer. Parameters influencing aerodynamic flows and transition and influence of boundary layers on outer potential flow are presented, along with associated stall and drag mechanisms. Numerical solution techniques and exercises are included. The major focus of 16.13 is on boundary layers, and boundary layer theory subject to various flow assumptions, such as compressibility, turbulence, dimensionality, and heat transfer. Parameters influencing aerodynamic flows and transition and influence of boundary layers on outer potential flow are presented, along with associated stall and drag mechanisms. Numerical solution techniques and exercises are included.Subjects

aerodynamics | aerodynamics | viscous fluids | viscous fluids | viscosity | viscosity | fundamental theorem of kinematics | fundamental theorem of kinematics | convection | convection | vorticity | vorticity | strain | strain | Eulerian description | Eulerian description | Lagrangian description | Lagrangian description | conservation of mass | conservation of mass | continuity | continuity | conservation of momentum | conservation of momentum | stress tensor | stress tensor | newtonian fluid | newtonian fluid | circulation | circulation | Navier-Stokes | Navier-Stokes | similarity | similarity | dimensional analysis | dimensional analysis | thin shear later approximation | thin shear later approximation | TSL coordinates | TSL coordinates | boundary conditions | boundary conditions | shear later categories | shear later categories | local scaling | local scaling | Falkner-Skan flows | Falkner-Skan flows | solution techniques | solution techniques | finite difference methods | finite difference methods | Newton-Raphson | Newton-Raphson | integral momentum equation | integral momentum equation | Thwaites method | Thwaites method | integral kinetic energy equation | integral kinetic energy equation | dissipation | dissipation | asymptotic perturbation | asymptotic perturbation | displacement body | displacement body | transpiration | transpiration | form drag | form drag | stall | stall | interacting boundary layer theory | interacting boundary layer theory | stability | stability | transition | transition | small-perturbation | small-perturbation | Orr-Somemerfeld | Orr-Somemerfeld | temporal amplification | temporal amplification | spatial amplification | spatial amplification | Reynolds | Reynolds | Prandtl | Prandtl | turbulent boundary layer | turbulent boundary layer | wake | wake | wall layers | wall layers | inner variables | inner variables | outer variables | outer variables | roughness | roughness | Clauser | Clauser | Dissipation formula | Dissipation formula | integral closer | integral closer | turbulence modeling | turbulence modeling | transport models | transport models | turbulent shear layers | turbulent shear layers | compressible then shear layers | compressible then shear layers | compressibility | compressibility | temperature profile | temperature profile | heat flux | heat flux | 3D boundary layers | 3D boundary layers | crossflow | crossflow | lateral dilation | lateral dilation | 3D separation | 3D separation | constant-crossflow | constant-crossflow | 3D transition | 3D transition | compressible thin shear layers | compressible thin shear layersLicense

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 metadata18.024 Multivariable Calculus with Theory (MIT) 18.024 Multivariable Calculus with Theory (MIT)

Description

This course is a continuation of 18.014. It covers the same material as 18.02 (Multivariable Calculus), but at a deeper level, emphasizing careful reasoning and understanding of proofs. There is considerable emphasis on linear algebra and vector integral calculus. This course is a continuation of 18.014. It covers the same material as 18.02 (Multivariable Calculus), but at a deeper level, emphasizing careful reasoning and understanding of proofs. There is considerable emphasis on linear algebra and vector integral calculus.Subjects

linear algebra | linear algebra | vector integral calculus | vector integral calculus | Calculus of several variables | Calculus of several variables | Vector algebra in 3-space | Vector algebra in 3-space | determinants | determinants | matrices | matrices | Vector-valued functions of one variable | Vector-valued functions of one variable | space motion | space motion | Scalar functions of several variables | Scalar functions of several variables | partial differentiation | partial differentiation | gradient | gradient | optimization techniques | optimization techniques | Double integrals and line integrals in the plane | Double integrals and line integrals in the plane | exact differentials and conservative fields | exact differentials and conservative fields | Green's theorem and applications | Green's theorem and applications | triple integrals | triple integrals | line and surface integrals in space | line and surface integrals in space | Divergence theorem | Divergence theorem | Stokes' theorem | Stokes' theorem | applications | applicationsLicense

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 metadata8.044 Statistical Physics I (MIT) 8.044 Statistical Physics I (MIT)

Description

This course offers an introduction to probability, statistical mechanics, and thermodynamics. Numerous examples are used to illustrate a wide variety of physical phenomena such as magnetism, polyatomic gases, thermal radiation, electrons in solids, and noise in electronic devices.This course is an elective subject in MIT’s undergraduate Energy Studies Minor. This Institute-wide program complements the deep expertise obtained in any major with a broad understanding of the interlinked realms of science, technology, and social sciences as they relate to energy and associated environmental challenges. This course offers an introduction to probability, statistical mechanics, and thermodynamics. Numerous examples are used to illustrate a wide variety of physical phenomena such as magnetism, polyatomic gases, thermal radiation, electrons in solids, and noise in electronic devices.This course is an elective subject in MIT’s undergraduate Energy Studies Minor. This Institute-wide program complements the deep expertise obtained in any major with a broad understanding of the interlinked realms of science, technology, and social sciences as they relate to energy and associated environmental challenges.Subjects

probability | probability | statistical mechanics | statistical mechanics | thermodynamics | thermodynamics | random variables | random variables | joint and conditional probability densities | joint and conditional probability densities | functions of a random variable | functions of a random variable | macroscopic variables | macroscopic variables | thermodynamic equilibrium | thermodynamic equilibrium | fundamental assumption of statistical mechanics | fundamental assumption of statistical mechanics | microcanonical and canonical ensembles | microcanonical and canonical ensembles | First | second | and third laws of thermodynamics | First | second | and third laws of thermodynamics | magnetism | magnetism | polyatomic gases | polyatomic gases | thermal radiation | thermal radiation | electrons in solids | electrons in solids | noise in electronic devices | noise in electronic devicesLicense

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 lectures. This course introduces students to the modeling, quantification, and analysis of uncertainty. The tools of probability theory, and of the related field of statistical inference, are the keys for being able to analyze and make sense of data. These tools underlie important advances in many fields, from the basic sciences to engineering and management. Includes audio/video content: AV lectures. This course introduces students to the modeling, quantification, and analysis of uncertainty. The tools of probability theory, and of the related field of statistical inference, are the keys for being able to analyze and make sense of data. These tools underlie important advances in many fields, from the basic sciences to engineering and management.Subjects

probability | probability | probability models | probability models | bayes rule | bayes rule | discrete random variables | discrete random variables | continuous random variables | continuous random variables | bernoulli process | bernoulli process | poisson process | poisson process | markov chains | markov chains | central limit theorem | central limit theorem | statistical inference | statistical inferenceLicense

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 the student into the use of quantitative techniques aimed at the empirical analysis of causal relationships between economic variables. The main tool is the linear regression model, which is discussed both at the theoretical and at the applied level. The course emphasizes the empirical analysis, for which it uses real economic data and open econometric software. This course introduces the student into the use of quantitative techniques aimed at the empirical analysis of causal relationships between economic variables. The main tool is the linear regression model, which is discussed both at the theoretical and at the applied level. The course emphasizes the empirical analysis, for which it uses real economic data and open econometric software.Subjects

Two-Stage Least Squares | Two-Stage Least Squares | Causal effect | Causal effect | TSLS | TSLS | Heteroskedasticity | Heteroskedasticity | Qualitative variables | Qualitative variables | Fundamentos del Analisis Economico | Fundamentos del Analisis Economico | OLS | OLS | ía y Derecho | ía y Derecho | Endogeneity | Endogeneity | Time series | Time series | IV | IV | ía | ía | ón de Empresas | ón de Empresas | Autocorrelation | Autocorrelation | Linear regression | Linear regression | 2SLS | 2SLS | Instrumental variables | Instrumental variables | Grado en Finanzas y Contabilidad | Grado en Finanzas y Contabilidad | Economia Aplicada | Economia Aplicada | Conditional mean | Conditional mean | Robust inference | Robust inference | 2012 | 2012 | Ordinary Least Squares | Ordinary Least SquaresLicense

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See all metadata8.044 Statistical Physics I (MIT) 8.044 Statistical Physics I (MIT)

Description

This course offers an introduction to probability, statistical mechanics, and thermodynamics. Numerous examples are used to illustrate a wide variety of physical phenomena such as magnetism, polyatomic gases, thermal radiation, electrons in solids, and noise in electronic devices. This course offers an introduction to probability, statistical mechanics, and thermodynamics. Numerous examples are used to illustrate a wide variety of physical phenomena such as magnetism, polyatomic gases, thermal radiation, electrons in solids, and noise in electronic devices.Subjects

probability | probability | statistical mechanics | statistical mechanics | thermodynamics | thermodynamics | random variables | random variables | joint and conditional probability densities | joint and conditional probability densities | functions of a random variable | functions of a random variable | macroscopic variables | macroscopic variables | thermodynamic equilibrium | thermodynamic equilibrium | fundamental assumption of statistical mechanics | fundamental assumption of statistical mechanics | microcanonical and canonical ensembles | microcanonical and canonical ensembles | First | First | second | second | and third laws of thermodynamics | and third laws of thermodynamics | magnetism | magnetism | polyatomic gases | polyatomic gases | hermal radiation | hermal radiation | thermal radiation | thermal radiation | electrons in solids | electrons in solids | and noise in electronic devices | and noise in electronic devices | First | second | and third laws of thermodynamics | First | second | and third laws of 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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See all metadata8.044 Statistical Physics I (MIT) 8.044 Statistical Physics I (MIT)

Description

Introduction to probability, statistical mechanics, and thermodynamics. Random variables, joint and conditional probability densities, and functions of a random variable. Concepts of macroscopic variables and thermodynamic equilibrium, fundamental assumption of statistical mechanics, microcanonical and canonical ensembles. First, second, and third laws of thermodynamics. Numerous examples illustrating a wide variety of physical phenomena such as magnetism, polyatomic gases, thermal radiation, electrons in solids, and noise in electronic devices. Concurrent enrollment in 8.04, Quantum Physics I, is recommended. Introduction to probability, statistical mechanics, and thermodynamics. Random variables, joint and conditional probability densities, and functions of a random variable. Concepts of macroscopic variables and thermodynamic equilibrium, fundamental assumption of statistical mechanics, microcanonical and canonical ensembles. First, second, and third laws of thermodynamics. Numerous examples illustrating a wide variety of physical phenomena such as magnetism, polyatomic gases, thermal radiation, electrons in solids, and noise in electronic devices. Concurrent enrollment in 8.04, Quantum Physics I, is recommended.Subjects

probability | probability | statistical mechanics | statistical mechanics | thermodynamics | thermodynamics | random variables | random variables | joint and conditional probability densities | joint and conditional probability densities | functions of a random variable | functions of a random variable | macroscopic variables | macroscopic variables | thermodynamic equilibrium | thermodynamic equilibrium | fundamental assumption of statistical mechanics | fundamental assumption of statistical mechanics | microcanonical and canonical ensembles | microcanonical and canonical ensembles | First | First | second | second | and third laws of thermodynamics | and third laws of thermodynamics | magnetism | magnetism | polyatomic gases | polyatomic gases | hermal radiation | hermal radiation | thermal radiation | thermal radiation | electrons in solids | electrons in solids | and noise in electronic devices | and noise in electronic devices | First | second | and third laws of thermodynamics | First | second | and third laws of 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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See all metadata8.044 Statistical Physics I (MIT) 8.044 Statistical Physics I (MIT)

Description

This course offers an introduction to probability, statistical mechanics, and thermodynamics. Numerous examples are used to illustrate a wide variety of physical phenomena such as magnetism, polyatomic gases, thermal radiation, electrons in solids, and noise in electronic devices. This course offers an introduction to probability, statistical mechanics, and thermodynamics. Numerous examples are used to illustrate a wide variety of physical phenomena such as magnetism, polyatomic gases, thermal radiation, electrons in solids, and noise in electronic devices.Subjects

probability | probability | statistical mechanics | statistical mechanics | thermodynamics | thermodynamics | random variables | random variables | joint and conditional probability densities | joint and conditional probability densities | functions of a random variable | functions of a random variable | macroscopic variables | macroscopic variables | thermodynamic equilibrium | thermodynamic equilibrium | fundamental assumption of statistical mechanics | fundamental assumption of statistical mechanics | microcanonical and canonical ensembles | microcanonical and canonical ensembles | First | First | second | second | and third laws of thermodynamics | and third laws of thermodynamics | magnetism | magnetism | polyatomic gases | polyatomic gases | hermal radiation | hermal radiation | thermal radiation | thermal radiation | electrons in solids | electrons in solids | and noise in electronic devices | and noise in electronic devices | First | second | and third laws of thermodynamics | First | second | and third laws of 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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See all metadataIntroduction to OO Programming in Java - Classes and arithmetic

Description

This visual aid forms part of the "Classes and arithmetic" topic in the Introduction to OO Programming in Java module.Subjects

ukoer | variables visual guide | java classes | variables | attributes | arithmetic | java class | classes and arithmetic | classes | class | java | object-oriented programming | programming | object-oriented | java classes visual aid | variables visual aid | attributes visual aid | arithmetic visual aid | java class visual aid | classes and arithmetic visual aid | classes visual aid | class visual aid | java visual aid | object-oriented programming visual aid | programming visual aid | object-oriented visual aid | g622 | oop | oo | oo programming | g622 visual aid | oop visual aid | oo visual aid | oo programming visual aid | Computer science | I100License

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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See all metadata18.440 Probability and Random Variables (MIT) 18.440 Probability and Random Variables (MIT)

Description

This course introduces students to probability and random variables. Topics include distribution functions, binomial, geometric, hypergeometric, and Poisson distributions. The other topics covered are uniform, exponential, normal, gamma and beta distributions; conditional probability; Bayes theorem; joint distributions; Chebyshev inequality; law of large numbers; and central limit theorem. This course introduces students to probability and random variables. Topics include distribution functions, binomial, geometric, hypergeometric, and Poisson distributions. The other topics covered are uniform, exponential, normal, gamma and beta distributions; conditional probability; Bayes theorem; joint distributions; Chebyshev inequality; law of large numbers; and central limit theorem.Subjects

Probability spaces | Probability spaces | random variables | random variables | distribution functions | distribution functions | Binomial | Binomial | geometric | geometric | hypergeometric | hypergeometric | Poisson distributions | Poisson distributions | Uniform | Uniform | exponential | exponential | normal | normal | gamma and beta distributions | gamma and beta distributions | Conditional probability | Conditional probability | Bayes theorem | Bayes theorem | joint distributions | joint distributions | Chebyshev inequality | Chebyshev inequality | law of large numbers | law of large numbers | and central limit theorem. | and central limit theorem.License

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 metadata6.436J Fundamentals of Probability (MIT) 6.436J Fundamentals of Probability (MIT)

Description

This is a course on the fundamentals of probability geared towards first or second-year graduate students who are interested in a rigorous development of the subject. The course covers most of the topics in MIT course 6.431 but at a faster pace and in more depth. Topics covered include: probability spaces and measures; discrete and continuous random variables; conditioning and independence; multivariate normal distribution; abstract integration, expectation, and related convergence results; moment generating and characteristic functions; Bernoulli and Poisson processes; finite-state Markov chains; convergence notions and their relations; and limit theorems. Familiarity with elementary notions in probability and real analysis is desirable. This is a course on the fundamentals of probability geared towards first or second-year graduate students who are interested in a rigorous development of the subject. The course covers most of the topics in MIT course 6.431 but at a faster pace and in more depth. Topics covered include: probability spaces and measures; discrete and continuous random variables; conditioning and independence; multivariate normal distribution; abstract integration, expectation, and related convergence results; moment generating and characteristic functions; Bernoulli and Poisson processes; finite-state Markov chains; convergence notions and their relations; and limit theorems. Familiarity with elementary notions in probability and real analysis is desirable.Subjects

Introduction to probability theory | Introduction to probability theory | Probability spaces and measures | Probability spaces and measures | Discrete and continuous random variables | Discrete and continuous random variables | Conditioning and independence | Conditioning and independence | Multivariate normal distribution | Multivariate normal distribution | Abstract integration | Abstract integration | expectation | expectation | and related convergence results | and related convergence results | Moment generating and characteristic functions | Moment generating and characteristic functions | Bernoulli and Poisson process | Bernoulli and Poisson process | Finite-state Markov chains | Finite-state Markov chains | Convergence notions and their relations | Convergence notions and their relations | Limit theorems | Limit theorems | Familiarity with elementary notions in probability and real analysis is desirable | Familiarity with elementary notions in probability and real analysis is desirableLicense

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 6-unit P/D/F course will provide a gentle introduction to programming using Python for highly motivated students with little or no prior experience in programming computers over the first two weeks of IAP. The course will focus on planning and organizing programs, as well as the grammar of the Python programming language. Lectures will be interactive, featuring in-class exercises with lots of support from the course staff. This class is designed to help prepare students for 6.01 Introduction to EECS I. 6.01 assumes some knowledge of Python upon entering; the course material for 6.189 has been specially designed to make sure that concepts important to 6.01 are covered. This course is offered during the Independent Activities Period (IAP), which is a special 4-week term at MIT that runs This 6-unit P/D/F course will provide a gentle introduction to programming using Python for highly motivated students with little or no prior experience in programming computers over the first two weeks of IAP. The course will focus on planning and organizing programs, as well as the grammar of the Python programming language. Lectures will be interactive, featuring in-class exercises with lots of support from the course staff. This class is designed to help prepare students for 6.01 Introduction to EECS I. 6.01 assumes some knowledge of Python upon entering; the course material for 6.189 has been specially designed to make sure that concepts important to 6.01 are covered. This course is offered during the Independent Activities Period (IAP), which is a special 4-week term at MIT that runsSubjects

python | python | programming | programming | introduction to programming | introduction to programming | programming for beginners | programming for beginners | variables | variables | operators | operators | control flow | control flow | functions | functions | strings | strings | lists | lists | environment diagrams | environment diagrams | list comprehensions | list comprehensions | hangman | hangman | dictionaries | dictionaries | graphics | graphics | python graphics | python graphics | objects | objects | oop | oop | inheritance | inheritance | tetris | tetris | tetris game | tetris gameLicense

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 offered both to undergraduates (6.041) and graduates (6.431), but the assignments differ. 6.041/6.431 introduces students to the modeling, quantification, and analysis of uncertainty. Topics covered include: formulation and solution in sample space, random variables, transform techniques, simple random processes and their probability distributions, Markov processes, limit theorems, and elements of statistical inference. This course is offered both to undergraduates (6.041) and graduates (6.431), but the assignments differ. 6.041/6.431 introduces students to the modeling, quantification, and analysis of uncertainty. Topics covered include: formulation and solution in sample space, random variables, transform techniques, simple random processes and their probability distributions, Markov processes, limit theorems, and elements of statistical inference.Subjects

probabilistic systems | probabilistic systems | probabilistic systems analysis | probabilistic systems analysis | applied probability | applied probability | uncertainty | uncertainty | uncertainty modeling | uncertainty modeling | uncertainty quantification | uncertainty quantification | analysis of uncertainty | analysis of uncertainty | uncertainty analysis | uncertainty analysis | sample space | sample space | random variables | random variables | transform techniques | transform techniques | simple random processes | simple random processes | probability distribution | probability distribution | Markov process | Markov process | limit theorem | limit theorem | statistical inference | statistical inferenceLicense

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 focus on the environment of firms with particular emphasis on economic variables such as GNP, inflation, interest rates, exchange rates and international trade. The course is divided into five parts: The first presents the basic tools of macroeconomic management by focusing on historical episodes, particularly in the United States. The second looks at national economic strategies for development. The third section concentrates on the recent financial and currency crises in emerging markets. The fourth part looks at the problems faced by transition economies. Finally, the last module looks at challenges of developed countries. This course will focus on the environment of firms with particular emphasis on economic variables such as GNP, inflation, interest rates, exchange rates and international trade. The course is divided into five parts: The first presents the basic tools of macroeconomic management by focusing on historical episodes, particularly in the United States. The second looks at national economic strategies for development. The third section concentrates on the recent financial and currency crises in emerging markets. The fourth part looks at the problems faced by transition economies. Finally, the last module looks at challenges of developed countries.Subjects

economic variables | economic variables | GNP | GNP | inflation | inflation | interest rates | interest rates | exchange rates | exchange rates | international trade | international trade | macroeconomic management | macroeconomic management | history | history | historical episodes | historical episodesLicense

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 metadata6.094 Introduction to MATLABÂ® (MIT) 6.094 Introduction to MATLABÂ® (MIT)

Description

This course provides an aggressively gentle introduction to MATLAB®. It is designed to give students fluency in MATLAB, including popular toolboxes. The course consists of interactive lectures with a computer running MATLAB for each student. Problem-based MATLAB assignments are given which require significant time on MATLAB. This course is offered 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 provides an aggressively gentle introduction to MATLAB®. It is designed to give students fluency in MATLAB, including popular toolboxes. The course consists of interactive lectures with a computer running MATLAB for each student. Problem-based MATLAB assignments are given which require significant time on MATLAB. This course is offered 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

matlab | matlab | simulink | simulink | matlab programming | matlab programming | variables | variables | plotting | plotting | scripts | scripts | functions | functions | flow control | flow control | linear algebra | linear algebra | polynomials | polynomials | optimization | optimization | differential equations | differential equations | ode | ode | probability | probability | statistics | statistics | data structures | data structures | images | images | animation | animation | debugging | debugging | symbolic math | symbolic math | toolboxes | toolboxes | scope | scope | function block | function block | nervous system | nervous systemLicense

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 presents the concepts and techniques for solving partial differential equations (pde), with emphasis on nonlinear pde. This course presents the concepts and techniques for solving partial differential equations (pde), with emphasis on nonlinear pde.Subjects

partial differential equations (pde) | partial differential equations (pde) | nonlinear pde | nonlinear pde | Diffusion | Diffusion | dispersion | dispersion | Initial and boundary value problems | Initial and boundary value problems | Characteristics and shocks | Characteristics and shocks | Separation of variables | Separation of variables | transform methods | transform methods | Green's functions | Green's functions | Asymptotics | Asymptotics | geometrical theory | geometrical theory | Dimensional analysis | Dimensional analysis | self-similarity | self-similarity | traveling waves | traveling waves | Singular perturbation and boundary layers | Singular perturbation and boundary layers | Solitons | Solitons | Variational methods | Variational methods | Free-boundary problems | Free-boundary problems | fluid dynamics | fluid dynamics | electrical engineering | electrical engineering | mechanical engineering | mechanical engineering | materials science | materials science | quantum mechanics | quantum 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 metadataESD.70J Engineering Economy Module (MIT) ESD.70J Engineering Economy Module (MIT)

Description

This intensive micro-subject provides the necessary skills in Microsoft® Excel spreadsheet modeling for ESD.71 – Engineering Systems Analysis for Design. Its purpose is to bring entering students up to speed on some of the advanced techniques that we routinely use in analysis. It is motivated by our experience that many students only have an introductory knowledge of Excel, and thus waste a lot of time thrashing about unproductively. Many people think they know Excel, but overlook many efficient tools – such as Data Table and Goal Seek. It is also useful for a variety of other subjects.NoteThis MIT OpenCourseWare site is based on the materials from Professor de Neufville's ESD.70J Web site. This intensive micro-subject provides the necessary skills in Microsoft® Excel spreadsheet modeling for ESD.71 – Engineering Systems Analysis for Design. Its purpose is to bring entering students up to speed on some of the advanced techniques that we routinely use in analysis. It is motivated by our experience that many students only have an introductory knowledge of Excel, and thus waste a lot of time thrashing about unproductively. Many people think they know Excel, but overlook many efficient tools – such as Data Table and Goal Seek. It is also useful for a variety of other subjects.NoteThis MIT OpenCourseWare site is based on the materials from Professor de Neufville's ESD.70J Web site.Subjects

excel | excel | spreadsheet | spreadsheet | modeling | modeling | dynamic modeling | dynamic modeling | analysis | analysis | data table | data table | goal seek | goal seek | sensitivity analysis | sensitivity analysis | simulation | simulation | random number generator | random number generator | counting | counting | modeling uncertainties | modeling uncertainties | random variables | random variables | statistical package | statistical package | flexibility | flexibility | contingency rules | contingency rules | excel solver | excel solver | solver | solverLicense

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 metadata18.440 Probability and Random Variables (MIT) 18.440 Probability and Random Variables (MIT)

Description

This course introduces students to probability and random variables. Topics include distribution functions, binomial, geometric, hypergeometric, and Poisson distributions. The other topics covered are uniform, exponential, normal, gamma and beta distributions; conditional probability; Bayes theorem; joint distributions; Chebyshev inequality; law of large numbers; and central limit theorem. This course introduces students to probability and random variables. Topics include distribution functions, binomial, geometric, hypergeometric, and Poisson distributions. The other topics covered are uniform, exponential, normal, gamma and beta distributions; conditional probability; Bayes theorem; joint distributions; Chebyshev inequality; law of large numbers; and central limit theorem.Subjects

Probability spaces | Probability spaces | random variables | random variables | distribution functions | distribution functions | Binomial | Binomial | geometric | geometric | hypergeometric | hypergeometric | Poisson distributions | Poisson distributions | Uniform | Uniform | exponential | exponential | normal | normal | gamma and beta distributions | gamma and beta distributions | Conditional probability | Conditional probability | Bayes theorem | Bayes theorem | joint distributions | joint distributions | Chebyshev inequality | Chebyshev inequality | law of large numbers | law of large numbers | central limit theorem | central limit 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 metadata18.175 Theory of Probability (MIT) 18.175 Theory of Probability (MIT)

Description

This course covers the laws of large numbers and central limit theorems for sums of independent random variables. It also analyzes topics such as the conditioning and martingales, the Brownian motion and the elements of diffusion theory. This course covers the laws of large numbers and central limit theorems for sums of independent random variables. It also analyzes topics such as the conditioning and martingales, the Brownian motion and the elements of diffusion theory.Subjects

Earth | Earth | Solar System | Solar System | Geophysics | Geophysics | Gravitational Field | Gravitational Field | Magnetic Field | Magnetic Field | Seismology | Seismology | Geodynamics | Geodynamics | Laws of large numbers | Laws of large numbers | central limit theorems for sums of independent random variables | central limit theorems for sums of independent random variables | conditioning and martingales | conditioning and martingales | Brownian motion and elements of diffusion theory | Brownian motion and elements of diffusion theory | functional limit theorems | functional limit theoremsLicense

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 deals primarily with equilibrium properties of macroscopic systems, basic thermodynamics, chemical equilibrium of reactions in gas and solution phase, and rates of chemical reactions.AcknowledgementsThe material for 5.60 has evolved over a period of many years, and therefore several faculty members have contributed to the development of the course contents. The following are known to have assisted in preparing the lecture notes available on OCW:Emeritus Professors of Chemistry: Robert A. Alberty, Carl W. Garland, Irwin Oppenheim, John S. Waugh.Professors of Chemistry: Moungi Bawendi, John M. Deutch, Robert W. Field, Robert G. Griffin, Keith A. Nelson, Robert J. Silbey, Jeffrey I. Steinfeld.Professor of Bioengineering and Computer Science: Bruce Tidor.Professor of Chemistry, Ri This subject deals primarily with equilibrium properties of macroscopic systems, basic thermodynamics, chemical equilibrium of reactions in gas and solution phase, and rates of chemical reactions.AcknowledgementsThe material for 5.60 has evolved over a period of many years, and therefore several faculty members have contributed to the development of the course contents. The following are known to have assisted in preparing the lecture notes available on OCW:Emeritus Professors of Chemistry: Robert A. Alberty, Carl W. Garland, Irwin Oppenheim, John S. Waugh.Professors of Chemistry: Moungi Bawendi, John M. Deutch, Robert W. Field, Robert G. Griffin, Keith A. Nelson, Robert J. Silbey, Jeffrey I. Steinfeld.Professor of Bioengineering and Computer Science: Bruce Tidor.Professor of Chemistry, RiSubjects

thermodynamics | thermodynamics | kinetics | kinetics | equilibrium | equilibrium | macroscopic systems | macroscopic systems | state variables | state variables | law of thermodynamics | law of thermodynamics | entropy | entropy | Gibbs function | Gibbs function | reaction rates | reaction rates | clapeyron | clapeyron | enthalpy | enthalpy | clausius | clausius | adiabatic | adiabatic | Hemholtz | Hemholtz | catalysis | catalysis | oscillators | oscillators | autocatalysis | autocatalysis | carnot cycle | carnot cycleLicense

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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