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Parametric and bootstrap estimations of confidence intervals for representative sample weights

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  • Statistical Estimation
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Summary

The essential problem in representative sampling is a comparison of analytical and sampling errors. Application of analysis of variance (ANOVA) to this problem seems less appropriate than regression analysis based on Wilsons formula, because in the first approach the total analytical error is commonly underestimated. Furthermore, regression analysis allows derivation of confidence intervals for representative sample weights. In this context a parametric and a non-parametric procedure are described.

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Author notes

  1. P. Heininger

    Present address: Bundesanstalt für Gewässerkunde, Außenstelle Berlin, Germany

Authors and Affiliations

  1. Fachbereich Chemie, Institut für analytische Chemie, Humboldt-Universität zu Berlin, O-1040, Berlin, Federal Republic of Germany

    R. Henrion, G. Henrion, K. Szukalski, I. Fabian, A. Thiesies & P. Heininger

Authors
  1. R. Henrion
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  2. G. Henrion
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  3. K. Szukalski
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  4. I. Fabian
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  5. A. Thiesies
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  6. P. Heininger
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Henrion, R., Henrion, G., Szukalski, K. et al. Parametric and bootstrap estimations of confidence intervals for representative sample weights. Fresenius J Anal Chem 340, 1–5 (1991). https://doi.org/10.1007/BF00324381

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  • DOI: https://doi.org/10.1007/BF00324381

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