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Correlations are used to explore the relationship between two variables. Such connections are interpreted to exhibit the linkages between two variables. However, the existence of causality should follow common sense. Thus, correlation is not proof of causality though causal factors and their effects are correlated. The causal relationship is taken to be valid only when the correlation is significant. Normally the level of significance P ≤ 0.05 (95% confidence interval). The R platform provides support for two methods of correlation analysis: 1) Pearson correlation; 2) Kendall-Spearman rank-correlation. The first one is parametric and the second one is based on the rank and therefore called a non-parametric test. The Pearson correlation is valid when the data follows the normal distribution. Before carrying out the correlation analysis, test assumptions are verified using the Shapiro-Wilk test for normality. The test assumes that the data follows a normal distribution (null hypothesis)