Applying Anova and Nonparametric Tests Simulation

Applying ANOVA and Nonparametric Tests Simulation

Using analysis of variance (ANOVA) helps companies identify where there are challenges and opportunities in the decision-making process. In the simulation the Praxidike Systems Corporation offered a chance for hands-on experience to apply ANOVA as well as other nonparametric tests when determining the cause of poor quality.

While working through the simulation, I learned to use the Kruskal-Wallis test when it is difficult to determine if the data has a normal distribution. I also learned the chi-square critical value is used for comparison in the Kruskal-Wallis test. The third thing I learned is the need to be aware of the three major assumptions; errors are random and independent of each other, each population has a normal distribution, and that all the populations have the same variance. Along with being aware of these assumption individuals need to know if these assumptions are met.

As a result of using this simulation, the concepts and analytical tools that will be able to be used in my workplace is the two-way ANOVA to find out if there is any interaction between any two variances. By performing different tests and analyzing them management can often determine where the problems start and how they can be fixed. My company will also benefit from reviewing current testing procedures and by ensuring that customer service and technical support staff have the proper training. It is important that the customer receive complete satisfaction at all times.

Based on my experience additional information to recommend to the key decision maker in the simulation to solve the challenge given would be to provide additional training to the programmers to increase their proficiency and competency. Another recommendation would be to double check their decisions to ensure the best solution is being used. According to the simulation the number of defects seems to have a strong impact on the variable client satisfaction and...