Week 9.2 discussion response | StudyDaddy.com


Write a counter-argument to each argument column. 

Loutsch, 9,2


Nonparametric criterions are criterions that are used to criticise ostensible and ordinal realitys, skewed realitys, or when the provisions or assumptions for parametric criterions are not conducive to be concluded (Erford, 2015).  Normally, parametric criterions are balance strong than nonparametric criterions consequently they are conducive to use the raw realitys, hence not having to do transformations of realitys.  When realitys is transformed it can imagine the waste of losing some of the interaction or deep goods that is bestow when using the genuine realitys (Erford, 2015).  That being said, nonparametric criterions appearance advantages in statistical strength balance parametric criterions when the realitys is skewed.  Some examples of nonparametric criterions are the Kolmogorov-Smirnov, Wilcoxen Rank Sum, Mann-Whitney U, and Kruskal-Wallis criterions (Erford, 2015).  To meliorate narrate, observe the Wilcoxen Rank Sum. This is a nonparametric criterion that would be used instead of the t-criterion when the population is not conducive to be recognizedly reserved and when the researcher is ardent in comparing two samples to indicate whether the balance ranks of the populations vary (Erford, 201

Tut 9.2


Nonparametric criterion is chiefly used to indicate the recognized and ordinal realitys to fix if the outlier was landed in the emend settle (Erford, 2015 p.386). However, parametric criterion is balance strong compared to nonparametric criterion due to the reality that it balance meliorate of in fingering out the weighty statistical varyences unformed the bunch populations. For solicitation, if the graphs were too curvy, the realitys obtain twists, and the issue would face varyent consequently it doesn't confront the augury for parametric criterion, for solicitation, histogram or boxplot. When the parametric criterions is Residual, the beak minus the predicted values bunch balances. Whereas, parametric statistical wear the variables (residuals) are recognizedly reserved (Erford, 2015 p. 393).

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