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Registro Completo |
Biblioteca(s): |
Embrapa Meio Ambiente. |
Data corrente: |
08/05/2007 |
Data da última atualização: |
13/08/2015 |
Tipo da produção científica: |
Artigo em Periódico Indexado |
Autoria: |
MAIA, A. de H. N.; MEINKE, H. |
Afiliação: |
ALINE DE HOLANDA NUNES MAIA, CNPMA; H. MEINKE, Queensland Department of Primary Industries and Fisheries, Emerging Technologies, Toowoomba, Australia. |
Título: |
From inferential statistics to climate knowledge . |
Ano de publicação: |
2006 |
Fonte/Imprenta: |
Advances in Geosciences, Hackensack, v. 6, p. 211-216, 2006. |
Idioma: |
Inglês |
Conteúdo: |
Climate variability and change are risk factors for climate sensitive activities such as agriculture. Managing these risks requires "climate knowledge", i.e. a sound understanding of causes and consequences of climate variability and knowledge of potential management options that are suitable in light of the climatic risks posed. Often such information about prognostic variables (e.g. yield, rainfall, run-off) is provided in probabilistic terms (e.g. via cumulative distribution functions, CDF), whereby the quantitative assessments of these alternative management options is based on such CDFs. Sound statistical approaches are needed in order to assess whether difference between such CDFs are intrinsic features of systems dynamics or chance events (i.e. quantifying evidences against an appropriate null hypothesis). Statistical procedures that rely on such a hypothesis testing framework are referred to as "inferential statistics" in contrast to descriptive statistics (e.g. mean, median, variance of population samples, skill scores). Here we report on the extension of some of the existing inferential techniques that provides more relevant and adequate information for decision making under uncertainty. |
Palavras-Chave: |
Avaliação quantitativa; Estatística descritiva; Fator de risco; Inferência; Mudanças climáticas; Prognóstico. |
Thesagro: |
Agricultura; Método Estatístico; Tomada de Decisão. |
Categoria do assunto: |
X Pesquisa, Tecnologia e Engenharia |
Marc: |
LEADER 01911naa a2200241 a 4500 001 1015270 005 2015-08-13 008 2006 bl uuuu u00u1 u #d 100 1 $aMAIA, A. de H. N. 245 $aFrom inferential statistics to climate knowledge .$h[electronic resource] 260 $c2006 520 $aClimate variability and change are risk factors for climate sensitive activities such as agriculture. Managing these risks requires "climate knowledge", i.e. a sound understanding of causes and consequences of climate variability and knowledge of potential management options that are suitable in light of the climatic risks posed. Often such information about prognostic variables (e.g. yield, rainfall, run-off) is provided in probabilistic terms (e.g. via cumulative distribution functions, CDF), whereby the quantitative assessments of these alternative management options is based on such CDFs. Sound statistical approaches are needed in order to assess whether difference between such CDFs are intrinsic features of systems dynamics or chance events (i.e. quantifying evidences against an appropriate null hypothesis). Statistical procedures that rely on such a hypothesis testing framework are referred to as "inferential statistics" in contrast to descriptive statistics (e.g. mean, median, variance of population samples, skill scores). Here we report on the extension of some of the existing inferential techniques that provides more relevant and adequate information for decision making under uncertainty. 650 $aAgricultura 650 $aMétodo Estatístico 650 $aTomada de Decisão 653 $aAvaliação quantitativa 653 $aEstatística descritiva 653 $aFator de risco 653 $aInferência 653 $aMudanças climáticas 653 $aPrognóstico 700 1 $aMEINKE, H. 773 $tAdvances in Geosciences, Hackensack$gv. 6, p. 211-216, 2006.
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