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Literature / Source Database:
Technometrics
Description
A Journal of Statistics for the Physical, Chemical, and Engineering Sciences
| Title (short) |
Technometrics |
| CODEN |
TCMTA2 |
| Languages |
English |
| First year |
1959 |
| Impact factor |
1.435 |
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Status
active
Indexing
Mathematical Reviews, abstracted in Applied Science & Technology Abstracts, and abstracted and indexed in The Engineering Index, COMPENDEX, and Applied Science & Technology Index.
Subject
Source type
Journal
Publisher
ISBN ISSN
0040-1706
E ISSN
1537-2723
First volume
43
Last volume
49+
Homepage
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Availability |
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| Text PDF |
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free access |
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| Text Html |
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for subscriber |
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| References |
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not available |
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| Abstracts |
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| TOC |
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Description
A journal of statistics for the physical, chemical, and engineering sciences jointly published by the American Society for Quality and the American Statistical Association.
Established in 1959, the mission of Technometrics has been to contribute to the development and use of statistical methods in the physical, chemical, and engineering sciences. Published papers describe new statistical techniques, illustrate innovative application of known statistical methods, or review methods, issues, or philosophy in a particular area of statistics or science, when such papers are consistent with the journal's objective. The methods presented in the journal have reflected changes in the discipline, such as improved computer technology and availability, resulting in highly flexible and computer-intensive approaches to scientific data analysis. Statistical approaches to screening, flexible modeling, pattern characterization, and change detection that were infeasible 20 years ago are now viable. There have also been shifts in basic attitudes about data analysis (e.g., less formal hypothesis testing, more fitted models via graphical analysis) and in how some of our important application areas are managed (e.g., quality assurance through robust design rather than detailed inspection).
Many of the problems in industry today concern the analysis of huge data sets that lead to improved quality or better understanding of the manufacturing or development process. The journal would like to encourage submissions that address problems of this nature and that take advantage of modern resources.
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