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NIST/SEMATECH e-Handbook of Statistical Methods (NIST Handbook 151)

The NIST/SEMATECH e-Handbook of Statistical Methods is a Web-based book written to help scientists and engineers incorporate statistical methods into their work as efficiently as possible. Ideally, it will serve as a reference which will help scientists and engineers design their own experiments and carry out the appropriate analyses when a statistician is not available to help. It is also hoped that it will serve as a useful educational tool that will help users of statistical methods and consumers of statistical information better understand statistical procedures and their underlying assumptions, and more clearly interpret scientific and engineering results stated in statistical terms.

About this Dataset

Updated: 2025-04-06
Metadata Last Updated: 2020-03-03 00:00:00
Date Created: N/A
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Dataset Owner: N/A

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Table representation of structured data
Title NIST/SEMATECH e-Handbook of Statistical Methods (NIST Handbook 151)
Description The NIST/SEMATECH e-Handbook of Statistical Methods is a Web-based book written to help scientists and engineers incorporate statistical methods into their work as efficiently as possible. Ideally, it will serve as a reference which will help scientists and engineers design their own experiments and carry out the appropriate analyses when a statistician is not available to help. It is also hoped that it will serve as a useful educational tool that will help users of statistical methods and consumers of statistical information better understand statistical procedures and their underlying assumptions, and more clearly interpret scientific and engineering results stated in statistical terms.
Modified 2020-03-03 00:00:00
Publisher Name National Institute of Standards and Technology
Contact mailto:[email protected]
Keywords Statistics , experimental design , time series , analysis of variance , quality control , reliability , graphics , exploratory data analysis , process characterization , regression , statistical modeling , model validation , uncertaintyanalysis
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