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Decision support using nonparametric statistics
This concise volume covers nonparametric statistics topics that most are most likely to be seen and used from a practical decision support perspective. While many degree programs require a course in parametric statistics, these methods are often inadequate for real-world decision making in business...
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Lenguaje: | eng |
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Springer
2018
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Acceso en línea: | https://dx.doi.org/10.1007/978-3-319-68264-8 http://cds.cern.ch/record/2303145 |
_version_ | 1780957316631232512 |
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author | Beatty, Warren |
author_facet | Beatty, Warren |
author_sort | Beatty, Warren |
collection | CERN |
description | This concise volume covers nonparametric statistics topics that most are most likely to be seen and used from a practical decision support perspective. While many degree programs require a course in parametric statistics, these methods are often inadequate for real-world decision making in business environments. Much of the data collected today by business executives (for example, customer satisfaction opinions) requires nonparametric statistics for valid analysis, and this book provides the reader with a set of tools that can be used to validly analyze all data, regardless of type. Through numerous examples and exercises, this book explains why nonparametric statistics will lead to better decisions and how they are used to reach a decision, with a wide array of business applications. Online resources include exercise data, spreadsheets, and solutions. |
id | cern-2303145 |
institution | Organización Europea para la Investigación Nuclear |
language | eng |
publishDate | 2018 |
publisher | Springer |
record_format | invenio |
spelling | cern-23031452021-04-21T18:55:25Zdoi:10.1007/978-3-319-68264-8http://cds.cern.ch/record/2303145engBeatty, WarrenDecision support using nonparametric statisticsMathematical Physics and MathematicsThis concise volume covers nonparametric statistics topics that most are most likely to be seen and used from a practical decision support perspective. While many degree programs require a course in parametric statistics, these methods are often inadequate for real-world decision making in business environments. Much of the data collected today by business executives (for example, customer satisfaction opinions) requires nonparametric statistics for valid analysis, and this book provides the reader with a set of tools that can be used to validly analyze all data, regardless of type. Through numerous examples and exercises, this book explains why nonparametric statistics will lead to better decisions and how they are used to reach a decision, with a wide array of business applications. Online resources include exercise data, spreadsheets, and solutions.Springeroai:cds.cern.ch:23031452018 |
spellingShingle | Mathematical Physics and Mathematics Beatty, Warren Decision support using nonparametric statistics |
title | Decision support using nonparametric statistics |
title_full | Decision support using nonparametric statistics |
title_fullStr | Decision support using nonparametric statistics |
title_full_unstemmed | Decision support using nonparametric statistics |
title_short | Decision support using nonparametric statistics |
title_sort | decision support using nonparametric statistics |
topic | Mathematical Physics and Mathematics |
url | https://dx.doi.org/10.1007/978-3-319-68264-8 http://cds.cern.ch/record/2303145 |
work_keys_str_mv | AT beattywarren decisionsupportusingnonparametricstatistics |