Statistical inference using bootstrap confidence intervals

Michael Wood

    Research output: Contribution to journalArticlepeer-review


    Bootstrap confidence intervals provide a way of quantifying the uncertainties in the inferences that can be drawn from a sample of data. The idea is to use a simulation, based on the actual data, to estimate the likely extent of sampling error. Michael Wood explains how simple bootstrapping works and explores some of its advantages.
    Original languageEnglish
    Pages (from-to)180-182
    Number of pages3
    Issue number4
    Publication statusPublished - Dec 2004


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