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- W2107974647 abstract "The most commonly used statistical procedures (t, F, chi-squared, ANOVA, regression) assume that samples have been taken at random from normal populations. In some cases the central limit theorem may provide a satisfactory approximation to normality, but, when samples are small, departures from normality can lead users of these procedures to false conclusions. In the paper on work-in-progress the authors describe the results of training an artificial neural network (ANN) to distinguish normal from non-normal samples for random samples of size 30. With little attempt at fine-tuning, the ANN achieves results comparable to those of the best known tests for normality. >" @default.
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- W2107974647 date "2002-12-04" @default.
- W2107974647 modified "2023-09-27" @default.
- W2107974647 title "Testing for normality using neural networks" @default.
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- W2107974647 doi "https://doi.org/10.1109/isuma.1990.151340" @default.
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