Abstract
/ It has been recognized for a long time that data transformation methods capable of achieving normality of distributions could have a crucial role in statistical analysis, especially towards an efficient application of techniques such as analysis of variance and multiple regression analysis. Normality is a basic assumption in many of the statistical methods used in the environmental sciences and is very often neglected. In this paper several techniques to test normality of distributions are proposed and analyzed. Confidence intervals and nonparametric tests are used and discussed. Basic and Box-Cox transformations are the suggested methods to achieve normal variables. Finally, we develop an application related to environmental data with atmospheric parameters and SO2 and particle concentrations. Results show that the analyzed transformations work well and are very useful to achieve normal distributions.KEY WORDS: Normal distribution; Kurtosis; Skewness; Confidence intervals; Box-Cox transformations; Nonparametric tests
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