Applying Anova and Non-Parametric Test Simulation

Topics: Analysis of variance, Non-parametric statistics, Normal distribution Pages: 1 (276 words) Published: February 15, 2011
Applying ANOVA and Non-Parametric Test Simulation

In business today, People can all appreciate customer satisfaction, employee involvement, and continuous improvement. In order to ensure quality, they are able to utilize a statistical tool in Operations Research and Total Quality Management, known as Analysis of Variance (ANOVA) or The Kruskal-Wallis Test. ANOVA assumes that each population being studied has a normal distribution, that the errors are random and independent of each other, and that all populations have the same variance. However, another nonparametric test Kruskal-Wallis assumes the data does not have a normal distribution and the data is ordinal and not quantitative.

In Applying ANOVA and Nonparametric Tests Simulation, I learned:

1. Defining characteristics of ANOVA Test and Kruskal-Wallis Test 2. If the data does not have normal distribution, use Kruskal-Wallis Test 3. One-way and two-way ANOVA analysis is a technique used to compare means of two or more groups; variables like client satisfaction and core competency

At the workplace, I would like to increase productivity, customer satisfaction, and continued education among sales and service representatives. In order to do this, I can apply the concepts of ANOVA analysis or Kruskal-Wallis. My recommendation to the key decision maker would be to double check if that is the best solution and that we might not over look the rest of variables. Proficiency is a necessity in insurance and financial industry. Your job depends on your competency.

References
University of Phoenix. (2011). Applying ANOVA and Nonparametric Tests Simulation. In Applying ANOVA and Nonparametric Tests Simulation. Retrieved January 25, 2011, from https://ecampus.phoenix.edu/secure/aapd/vendors/tata/UBAMsims/research2/res