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Título:
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Representing data distributions with kernel density estimates
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Autores:
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THOMPSON, MICHAEL
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Tipo de documento:
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texto impreso
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Editorial:
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Cambridge [REINO UNIDO] : Royal Society of Chemistry, 2006
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Colección:
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AMC Technical Brief, num. 4
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ISBN/ISSN/DL:
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29710
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Dimensiones:
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2 p.
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Nota general:
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ISBN:
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Langues:
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Inglés
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Clasificación:
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ANÁLISIS DE DATOS
GRÁFICO
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Resumen:
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Histograms are the usual vehicle for representing medium sized data distributions graphically, but they suffer from several defects. The kernel density estimate is an alternative computer-intensive method, which involves smoothing the data while retaining the overall structure. It is a good method of reconstructing an unknown population from a random sample of data, overcomes the problems of histograms and has many applications in analytical chemistry. An Excel add-in and Minitab macro for calculating kernel density estimates are available in AMC Software [1].
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