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Description

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

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

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

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See all metadata6.856J Randomized Algorithms (MIT) 6.856J Randomized Algorithms (MIT)

Description

This course examines how randomization can be used to make algorithms simpler and more efficient via random sampling, random selection of witnesses, symmetry breaking, and Markov chains. Topics covered include: randomized computation; data structures (hash tables, skip lists); graph algorithms (minimum spanning trees, shortest paths, minimum cuts); geometric algorithms (convex hulls, linear programming in fixed or arbitrary dimension); approximate counting; parallel algorithms; online algorithms; derandomization techniques; and tools for probabilistic analysis of algorithms. This course examines how randomization can be used to make algorithms simpler and more efficient via random sampling, random selection of witnesses, symmetry breaking, and Markov chains. Topics covered include: randomized computation; data structures (hash tables, skip lists); graph algorithms (minimum spanning trees, shortest paths, minimum cuts); geometric algorithms (convex hulls, linear programming in fixed or arbitrary dimension); approximate counting; parallel algorithms; online algorithms; derandomization techniques; and tools for probabilistic analysis of algorithms.Subjects

Randomized Algorithms | Randomized Algorithms | algorithms | algorithms | efficient in time and space | efficient in time and space | randomization | randomization | computational problems | computational problems | data structures | data structures | graph algorithms | graph algorithms | optimization | optimization | geometry | geometry | Markov chains | Markov chains | sampling | sampling | estimation | estimation | geometric algorithms | geometric algorithms | parallel and distributed algorithms | parallel and distributed algorithms | parallel and ditributed algorithm | parallel and ditributed algorithm | parallel and distributed algorithm | parallel and distributed algorithm | random sampling | random sampling | random selection of witnesses | random selection of witnesses | symmetry breaking | symmetry breaking | randomized computational models | randomized computational models | hash tables | hash tables | skip lists | skip lists | minimum spanning trees | minimum spanning trees | shortest paths | shortest paths | minimum cuts | minimum cuts | convex hulls | convex hulls | linear programming | linear programming | fixed dimension | fixed dimension | arbitrary dimension | arbitrary dimension | approximate counting | approximate counting | parallel algorithms | parallel algorithms | online algorithms | online algorithms | derandomization techniques | derandomization techniques | probabilistic analysis | probabilistic analysis | computational number theory | computational number theory | simplicity | simplicity | speed | speed | design | design | basic probability theory | basic probability theory | application | application | randomized complexity classes | randomized complexity classes | game-theoretic techniques | game-theoretic techniques | Chebyshev | Chebyshev | moment inequalities | moment inequalities | limited independence | limited independence | coupon collection | coupon collection | occupancy problems | occupancy problems | tail inequalities | tail inequalities | Chernoff bound | Chernoff bound | conditional expectation | conditional expectation | probabilistic method | probabilistic method | random walks | random walks | algebraic techniques | algebraic techniques | probability amplification | probability amplification | sorting | sorting | searching | searching | combinatorial optimization | combinatorial optimization | approximation | approximation | counting problems | counting problems | distributed algorithms | distributed algorithms | 6.856 | 6.856 | 18.416 | 18.416License

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

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This course is an introduction to discrete applied mathematics. Topics include probability, counting, linear programming, number-theoretic algorithms, sorting, data compression, and error-correcting codes. This is a Communication Intensive in the Major (CI-M) course, and thus includes a writing component. This course is an introduction to discrete applied mathematics. Topics include probability, counting, linear programming, number-theoretic algorithms, sorting, data compression, and error-correcting codes. This is a Communication Intensive in the Major (CI-M) course, and thus includes a writing component.Subjects

probability | probability | probability theory counting | probability theory counting | pigeonhole principle | pigeonhole principle | Van der Waerden's theorem | Van der Waerden's theorem | Chernoff bounds | Chernoff bounds | counting | counting | coding | coding | sampling | sampling | random sampling | random sampling | Catalan families | Catalan families | generating functions | generating functions | chord diagrams | chord diagrams | linear programming | linear programming | simplex method | simplex method | Zero-Sum matrix | Zero-Sum matrix | network flows | network flows | maximum flow problem | maximum flow problem | sorting algorithms | sorting algorithms | QUICKSORT | QUICKSORT | median finding | median finding | sorting networks | sorting networks | Batcher's algorithm | Batcher's algorithm | Euclid's algorithm | Euclid's algorithm | Chinese Remainder Theorem | Chinese Remainder Theorem | cryptography | cryptography | RSA code | RSA code | primaility testing | primaility testing | FFT | FFT | Fast Fourier Transform | Fast Fourier Transform | Shannon's coding theorems | Shannon's coding theorems | Lempel-Ziv codes | Lempel-Ziv codes | linear codes | linear codes | hamming code | hamming codeLicense

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

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See all metadata1.017 Computing and Data Analysis for Environmental Applications (MIT)

Description

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

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

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

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See all metadata1.017 Computing and Data Analysis for Environmental Applications (MIT)

Description

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

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

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

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See all metadata1.017 Computing and Data Analysis for Environmental Applications (MIT)

Description

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

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

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

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See all metadata1.017 Computing and Data Analysis for Environmental Applications (MIT)

Description

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

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

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

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See all metadata1.017 Computing and Data Analysis for Environmental Applications (MIT)

Description

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

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

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

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See all metadata1.017 Computing and Data Analysis for Environmental Applications (MIT)

Description

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

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

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

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See all metadata1.017 Computing and Data Analysis for Environmental Applications (MIT)

Description

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

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

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

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See all metadata1.017 Computing and Data Analysis for Environmental Applications (MIT)

Description

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

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

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

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See all metadata1.017 Computing and Data Analysis for Environmental Applications (MIT)

Description

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

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

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

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See all metadata1.017 Computing and Data Analysis for Environmental Applications (MIT)

Description

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

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

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

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See all metadata1.017 Computing and Data Analysis for Environmental Applications (MIT)

Description

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

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

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

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See all metadata1.017 Computing and Data Analysis for Environmental Applications (MIT)

Description

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

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

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

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See all metadata1.017 Computing and Data Analysis for Environmental Applications (MIT)

Description

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

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

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

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See all metadata1.017 Computing and Data Analysis for Environmental Applications (MIT)

Description

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

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

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

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See all metadata1.017 Computing and Data Analysis for Environmental Applications (MIT)

Description

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

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

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

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See all metadata1.017 Computing and Data Analysis for Environmental Applications (MIT)

Description

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

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

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

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See all metadata1.017 Computing and Data Analysis for Environmental Applications (MIT)

Description

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

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

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

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See all metadata1.017 Computing and Data Analysis for Environmental Applications (MIT)

Description

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

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

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

http://ocw.mit.edu/rss/all/mit-allthaicourses.xmlAttribution

Click to get HTML | Click to get attribution | Click to get URLAll metadata

See all metadata1.017 Computing and Data Analysis for Environmental Applications (MIT)

Description

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

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

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

http://ocw.mit.edu/rss/all/mit-allthaicourses.xmlAttribution

Click to get HTML | Click to get attribution | Click to get URLAll metadata

See all metadata1.017 Computing and Data Analysis for Environmental Applications (MIT)

Description

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

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

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

http://ocw.mit.edu/rss/all/mit-allthaicourses.xmlAttribution

Click to get HTML | Click to get attribution | Click to get URLAll metadata

See all metadata1.017 Computing and Data Analysis for Environmental Applications (MIT)

Description

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

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

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

http://ocw.mit.edu/rss/all/mit-allthaicourses.xmlAttribution

Click to get HTML | Click to get attribution | Click to get URLAll metadata

See all metadata1.017 Computing and Data Analysis for Environmental Applications (MIT)

Description

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

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

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

http://ocw.mit.edu/rss/all/mit-allthaicourses.xmlAttribution

Click to get HTML | Click to get attribution | Click to get URLAll metadata

See all metadata