Statistical methods for dividing sites into recommendation domains on the basis of experimental results
The purpose of identifying recommendation domains; must be for making recommendations from current data and for planning and interpreting future experiments. In a general sense every applied scientist must have a concept of a recommendation domain for his/her research. Some concept of the population to which results are relevant is integral to any research (even statistics!). The data on which the division of sites into groups for potential domains maybe some combination of (I) economic/sociological: based usually on surveys: (2) physical/meteorological/soils/vegetation: based on observation or on records from nearby available sources; (3) experimental results. There is also always potential for general qualitative judgment about site similarities. There are two stages to the identification of domains, which is a dynamic process rather than a permanent decision. We should separate the process of constructing groups from that of testing, or validating the group structure. The group construction may be attempted using any of the three forms of data. The validation process appears (to me) to be peculiar to experimental data because that data carries with it information about the precision of estimates calculated from the data. It would, of course, be possible to use the precision information inherent in experimental data to test groups (i.e. tentative domains) derived from other forms of data.
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| Format: | Book biblioteca |
| Language: | English |
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CIMMYT
[1990?]
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| Subjects: | AGRICULTURAL SCIENCES AND BIOTECHNOLOGY, COMPUTER APPLICATIONS, CROPPING SYSTEMS, DATA ANALYSIS, EXPERIMENTATION, SAMPLING, SPACING, STATISTICAL METHODS, FARMING SYSTEMS, |
| Online Access: | http://hdl.handle.net/10883/866 |
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dig-cimmyt-10883-8662022-09-21T16:39:11Z Statistical methods for dividing sites into recommendation domains on the basis of experimental results Mead, R. AGRICULTURAL SCIENCES AND BIOTECHNOLOGY COMPUTER APPLICATIONS CROPPING SYSTEMS DATA ANALYSIS EXPERIMENTATION SAMPLING SPACING STATISTICAL METHODS FARMING SYSTEMS COMPUTER APPLICATIONS CROPPING SYSTEMS DATA ANALYSIS EXPERIMENTATION SAMPLING SPACING STATISTICAL METHODS FARMING SYSTEMS The purpose of identifying recommendation domains; must be for making recommendations from current data and for planning and interpreting future experiments. In a general sense every applied scientist must have a concept of a recommendation domain for his/her research. Some concept of the population to which results are relevant is integral to any research (even statistics!). The data on which the division of sites into groups for potential domains maybe some combination of (I) economic/sociological: based usually on surveys: (2) physical/meteorological/soils/vegetation: based on observation or on records from nearby available sources; (3) experimental results. There is also always potential for general qualitative judgment about site similarities. There are two stages to the identification of domains, which is a dynamic process rather than a permanent decision. We should separate the process of constructing groups from that of testing, or validating the group structure. The group construction may be attempted using any of the three forms of data. The validation process appears (to me) to be peculiar to experimental data because that data carries with it information about the precision of estimates calculated from the data. It would, of course, be possible to use the precision information inherent in experimental data to test groups (i.e. tentative domains) derived from other forms of data. 41 pages 2012-01-06T05:06:18Z 2012-01-06T05:06:18Z [1990?] Book http://hdl.handle.net/10883/866 English CIMMYT Training Working Document CIMMYT manages Intellectual Assets as International Public Goods. The user is free to download, print, store and share this work. In case you want to translate or create any other derivative work and share or distribute such translation/derivative work, please contact CIMMYT-Knowledge-Center@cgiar.org indicating the work you want to use and the kind of use you intend; CIMMYT will contact you with the suitable license for that purpose. Open Access PDF Mexico CIMMYT |
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| topic |
AGRICULTURAL SCIENCES AND BIOTECHNOLOGY COMPUTER APPLICATIONS CROPPING SYSTEMS DATA ANALYSIS EXPERIMENTATION SAMPLING SPACING STATISTICAL METHODS FARMING SYSTEMS COMPUTER APPLICATIONS CROPPING SYSTEMS DATA ANALYSIS EXPERIMENTATION SAMPLING SPACING STATISTICAL METHODS FARMING SYSTEMS AGRICULTURAL SCIENCES AND BIOTECHNOLOGY COMPUTER APPLICATIONS CROPPING SYSTEMS DATA ANALYSIS EXPERIMENTATION SAMPLING SPACING STATISTICAL METHODS FARMING SYSTEMS COMPUTER APPLICATIONS CROPPING SYSTEMS DATA ANALYSIS EXPERIMENTATION SAMPLING SPACING STATISTICAL METHODS FARMING SYSTEMS |
| spellingShingle |
AGRICULTURAL SCIENCES AND BIOTECHNOLOGY COMPUTER APPLICATIONS CROPPING SYSTEMS DATA ANALYSIS EXPERIMENTATION SAMPLING SPACING STATISTICAL METHODS FARMING SYSTEMS COMPUTER APPLICATIONS CROPPING SYSTEMS DATA ANALYSIS EXPERIMENTATION SAMPLING SPACING STATISTICAL METHODS FARMING SYSTEMS AGRICULTURAL SCIENCES AND BIOTECHNOLOGY COMPUTER APPLICATIONS CROPPING SYSTEMS DATA ANALYSIS EXPERIMENTATION SAMPLING SPACING STATISTICAL METHODS FARMING SYSTEMS COMPUTER APPLICATIONS CROPPING SYSTEMS DATA ANALYSIS EXPERIMENTATION SAMPLING SPACING STATISTICAL METHODS FARMING SYSTEMS Mead, R. Statistical methods for dividing sites into recommendation domains on the basis of experimental results |
| description |
The purpose of identifying recommendation domains; must be for making recommendations from current data and for planning and interpreting future experiments. In a general sense every applied scientist must have a concept of a recommendation domain for his/her research. Some concept of the population to which results are relevant is integral to any research (even statistics!). The data on which the division of sites into groups for potential domains maybe some combination of (I) economic/sociological: based usually on surveys: (2) physical/meteorological/soils/vegetation: based on observation or on records from nearby available sources; (3) experimental results. There is also always potential for general qualitative judgment about site similarities. There are two stages to the identification of domains, which is a dynamic process rather than a permanent decision. We should separate the process of constructing groups from that of testing, or validating the group structure. The group construction may be attempted using any of the three forms of data. The validation process appears (to me) to be peculiar to experimental data because that data carries with it information about the precision of estimates calculated from the data. It would, of course, be possible to use the precision information inherent in experimental data to test groups (i.e. tentative domains) derived from other forms of data. |
| format |
Book |
| topic_facet |
AGRICULTURAL SCIENCES AND BIOTECHNOLOGY COMPUTER APPLICATIONS CROPPING SYSTEMS DATA ANALYSIS EXPERIMENTATION SAMPLING SPACING STATISTICAL METHODS FARMING SYSTEMS COMPUTER APPLICATIONS CROPPING SYSTEMS DATA ANALYSIS EXPERIMENTATION SAMPLING SPACING STATISTICAL METHODS FARMING SYSTEMS |
| author |
Mead, R. |
| author_facet |
Mead, R. |
| author_sort |
Mead, R. |
| title |
Statistical methods for dividing sites into recommendation domains on the basis of experimental results |
| title_short |
Statistical methods for dividing sites into recommendation domains on the basis of experimental results |
| title_full |
Statistical methods for dividing sites into recommendation domains on the basis of experimental results |
| title_fullStr |
Statistical methods for dividing sites into recommendation domains on the basis of experimental results |
| title_full_unstemmed |
Statistical methods for dividing sites into recommendation domains on the basis of experimental results |
| title_sort |
statistical methods for dividing sites into recommendation domains on the basis of experimental results |
| publisher |
CIMMYT |
| publishDate |
[1990?] |
| url |
http://hdl.handle.net/10883/866 |
| work_keys_str_mv |
AT meadr statisticalmethodsfordividingsitesintorecommendationdomainsonthebasisofexperimentalresults |
| _version_ |
1756086314894098432 |