Week 3 Anova Paper

Analysis of Variance Summary

Maria Smith

RES/342

October 24, 2011

Merry J Gallo

Analysis of Variance Summary

In today’s business world, people all over can appreciate employee involvement, customer satisfaction, and continuous improvement. These businesses use a tool called Analysis of Variance Test, which helps them ensure quality in various departments, as well in a variety of ways. Analysis of Variance assumes that all populations have the same variance, that all populations involved in the test have are normally distributed, and all errors are independent and random of each other. Whereas the other nonparametric test, Kruskal-Wallis is quite the opposite. It assumes that all data is not quantitative, but ordinal, and it assumes that the data is not normally distributed.

In the simulation I learned a few things about the different nonparametric tests. I now know the difference between the ANOVA Test and Kruskal-Wallis Test, as well as their defining components. I learned that if the data is not normally distributed we would then use the Kruskal-Wallis test. The third thing I learned while in the simulation was that when we are comparing two or more means, such as department efficiency and client satisfaction, the two-way ANOVA analysis would be used.

In my workplace, I want to increase education among all sales and service representatives, as well as productivity and customer satisfaction. By applying the Analysis of Variance or even the Kruskal-Wallis, I believe I can achieve that goal.

The challenges can be solved by properly conducting the essential step in simulation. The test must be an appropriate test, which should give an exact answer for the decision maker. So before applying the test the data should be checked whether it is an ordinal or nominal or interval or ratio, and the test should be conducted based on the data level of measurement.

Reference

University of Phoenix. (2011). Applying ANOVA and Nonparametric Tests...