{"id":27263,"date":"2024-01-08T20:08:45","date_gmt":"2024-01-08T19:08:45","guid":{"rendered":"https:\/\/transferencia.umh.es\/?p=27263"},"modified":"2026-10-07T09:52:54","modified_gmt":"2026-10-07T07:52:54","slug":"tratamiento-de-la-informacion-y-metodos-estadisticos","status":"publish","type":"post","link":"https:\/\/transferencia.umh.es\/en\/2024\/01\/08\/tratamiento-de-la-informacion-y-metodos-estadisticos\/","title":{"rendered":"Information Processing and Statistical Methods"},"content":{"rendered":"

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Information Processing and Statistical Methods<\/h1>\n

Use of statistical methods applied to populations, the environment, health and epidemiology. This approach addresses real-world problems through data science from a statistical modelling perspective, with the aim of improving interpretation, analysis, prediction and statistical inference.<\/h2>\n<\/header>\n
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TECHNOLOGY DESCRIPTION<\/h3>\n
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Tools and software for statistical data analysis. Statistical procedures for sample and experimental design, processing of survey, opinion poll and market research data, exploratory data analysis, quality control, statistical inference, prediction, uncertainty measurement, mapping of socio-economic, environmental and epidemiological indicators, modelling of hierarchical, spatial or temporal connection structures, and statistical processing of complex data.<\/p>\n<\/div>\n<\/section>\n

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TECHNOLOGY APPLICATIONS<\/h3>\n
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The knowledge offered can be used to design public policies and social programmes, appropriately allocate public funding, support regional planning, assist small businesses in decision-making, and inform environmental and ecological planning.<\/p>\n<\/div>\n<\/section>\n

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ADVANTAGES AND INNOVATIVE ASPECTS<\/h3>\n
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Statistical procedures for sample and experimental design, processing of survey, opinion poll and market research data, exploratory data analysis, quality control, statistical inference, prediction, uncertainty measurement, mapping of socio-economic, environmental or epidemiological indicators, modelling of hierarchical, spatial or temporal correlation structures, and statistical processing of complex data.<\/p>\n

Using these procedures, it is possible to anticipate and address climate-related problems in a specific area, as well as the spread of diseases or pollution in that area. They can also be used to anticipate the agricultural production of a specific farm and its water requirements.<\/p>\n<\/div>\n<\/section>\n

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DEVELOPMENT STAGE<\/h3>\n
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This knowledge is ready to be implemented by companies and institutions. The methodology is based on applied theoretical research using real-world models, ensuring its applicability and effectiveness.<\/p>\n<\/div>\n<\/section>\n

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APPLICATION SECTORS<\/h3>\n
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