Using analysis of variance (ANOVA) in the quality management process will help a company identify where the challenges and opportunities lie in the process and help to guide those in the decision-making role on the necessary improvements to be implemented to create the desired change. The Praxidike Systems corporation has identified opportunities with on-time delivery and customer satisfaction. Applying ANOVA as well as other nonparametric tests, such as the Kruskal-Wallis test, allows the company to determine the causes of the poor quality.
In completing the "Applying ANOVA and Nonparametric Tests" simulation, I learned the following three lessons relative to the analysis:1.Accurately identifying a one-way analysis versus a two-way analysis is crucial to performing the tests.
2.Use the Kruskal-Wallis test if unable to determine if the data has a normal distribution.
3. Be aware of the three major assumptions and know if they are met:Ã¢ÂÂ¢Errors are random and independent of each other.
Ã¢ÂÂ¢Each population has a normal distributionÃ¢ÂÂ¢All the populations have the same variance.
(Lind, Marchal, & Wathen, 2004).
A one-way analysis only looks at one variable such as client satisfaction. A two-way analysis compares two variables such as project complexity and employee competency. The variables are compared against the variances such as high, medium, and low. Being able to separate the variables from the variances and not confusing them will ensure the test results are accurately considered. Also, knowing if the data has a normal distribution will determine if ANOVA is used or if another nonparametric test is used. If the person is unsure, a nonparametric test such as the Kruskal-Wallis test should be used along with the chi square goodness of fit test.
After completing the simulation and being able to link the data analysis more realistically to the business world, I could envision...