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Interval frequency distributions used as a secondary data do not provide full information about the distribution of the variable of interest. In order to estimate quantiles, it is common to use one of interpolation formulae based on the assumption that the distribution of the variable is uniformly distributed in each interval. This assumption, if not met, may result in large bias. Author suggests using different formulae, which do not require uniform distribution in intervals, and depend on the frequencies of neighbouring intervals. Simulation experiments were applied for the normal and lognormal distributions to assess efficiency of both kinds of interpolation formulae. Different population sizes and different numbers of intervals in the frequency distribution were also considered.
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