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The parametric test is based on assumptions on the population distribution. However nonparametric does not make assumptions; this test ranks the outcome variable from low to high and analyzing the ranks. For example, when doing the coffee time simulation I learned that when there is a high degree of correlation between two variables it is a good idea to remove one. In the simulation I chose to remove the value X3: 0.657 since it is the one correlated with greater number of independent variables in the model. The reason why this value had to be removed is because it distorts the value of the regression coefficients...
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