Apply Anova

The use of analysis of variance (ANOVA) in quality management will help an organization to identify the possible challenges and opportunities within the process and to assist in guiding the individuals involved with the decision-making role on the necessary enhancements that need to be implemented to create the requested results. The Praxidike Systems Corporation has identified opportunities with customer service satisfaction and on-time delivery. Applying ANOVA as well as other nonparametric tests, such as the Kruskal-Wallis test, allows the organization to determine the foundation of the current poor quality.

After the completion of the “Applying ANOVA and Nonparametric Tests” simulation, I have learned the following three lessons in relation to the analysis: accurately identifying a one-way analysis versus a two-way analysis is crucial to performing the tests; use the Kruskal-Wallis test if unable to determine if the data has a normal distribution; and to be aware of the three major assumptions and know if they are met. The three major assumptions are: errors are random and independent of each other, each population has a normal distribution, and all the populations have the same variance (Lind, Marchal, & Wathen, 2004).

A one-way analysis only looks at one variable, for instance, customer satisfaction. A two-way analysis compares two variables such as project intricacy and employee experience. The variables are compared to the variances, for example- high, medium, and low. Being capable of separating the variables from the variances and not confusing the two will ensure the test results are considered accurately. Furthermore, the knowledge of 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.