Statistical Tests for Normal Family in Presence of Outlying Observations

A package of programs in the Fortran software is available for statistical analysis of normal data in presence of outlying observations. At first the Bol’shev test, based on the Chauvenet rule, is applied for detecting all outlying observations in a sample. After the chi-squared type test, based on the statistic of Nikulin-Rao-Robson-Moore with the Neyman-Pearson classes for grouping of data, is applied for testing of normality. We include a practical application of our software to treat the data of Milliken and the data of Daniel. The power of the test for testing normality against the family of logistic distributions, formed on the Neyman-Pearson classes, is also studied.