Analysis of a Problem Using Various Visions

 In this paper an applied problem, where the response of interest is the number of success in a specific experiment, is considered and by various visions is studied. The effects of outlier values of response on results of a regression analysis are so important to be studied. For this reason, using diagnostic methods, outlier response values are recognized. It is shown that use of arc-sine transformation many be misleading in recognizing response outliers. If deleting of outliers is not possible, use of robust modeling approach is suggested. Method of maximum likelihood for estimating parameters in generalized linear model, transformation method and also pseudo-likelihood method are not robust. A method, which is called robust pseudo-likelihood and leads to robust results, is reviewed and a simpler method of computing P-values for model selection is presented. Various approaches for modeling are also compared in the applied example.