Applying ANOVA and Nonparametric Test

Applying ANOVA and Nonparametric test
In the simulation, I selected the Kruskal-Wallis test which is used when it is difficult to meet all of the assumptions of ANOVA. The Kruskal-Wallis test is a nonparametric alternative to one way ANOVA. This test is used to compare three or more samples, to test the null hypothesis that the different samples in the comparison drawn from the same distribution or from distributions with the same median. Interpretation of the Kruskal-Wallis test is basically similar to that of the parametric one way ANOVA however; nonparametric tests are usually simpler to calculate. We use ANOVA and Nonparametric tests in everyday situations, we rely on statistical techniques whether to determine a baseball score or to find out why a crop is failing.
Being able to apply the concepts and analytical tools in the workplace should not be a difficult one. In the simulation, I made suggestions to review project plans and effort estimation, review testing procedures and increase channels for effective communication. I should have set up a two-way ANOVA with a schedule variance as factor A and number of defects as factor B which I then would have been able to conclude that there was a difference in the means due to a schedule variance, number of defects, and the interaction of schedule variance and number of defects. With ANOVA, you have a more flexible as well as a more powerful technique that can be applied to more complex research issues. At the end of the simulation, I had a better understanding of ANOVA and that it allows us to detect interaction effects between variables.
The recommendations that I would make to the key decision maker in the simulation would be to check on the number of defects which seems to have a strong positive correlation with the variable client satisfaction. I would also suggest that they set up competency levels for a project depending on the skill requirements, and to review the project plan and account for any scope