Anova and Nonparametric

Applying ANOVA and Nonparametric Tests Simulation

Applying ANOVA and Nonparametric Tests

ANOVA helps assume that each population being studied has a normal distribution. All the populations have the same variance where errors are random and independent from each other. Through the ANOVA and Nonparametric tests I learned the critical tools that I am able to use and the tools that are being used in my workplace. In this simulation, I took the role as the Quality Assurance Manager for Praxidike Systems. The position will make me responsible to make very important decisions by making sure that deliveries arrive on time and that the clients are satisfied. I had to decide which type of test to use. In order to be able to use ANOVA I have to make three major assumptions: errors are random and independent of each other, each population has a normal distribution, and all populations have the same variance. The simulation also helped me learned three lessons: accurately identifying a one-way analysis versus a two-way analysis is crucial to performing the tests, using the Kruskal-Wallis test if unable to determine if the data has a normal distribution, and being aware of the three major assumptions.

In my workplace I will need to use certain concepts and analytics tools. These can be anywhere from observing, analyzing, gathering data, decision making, critical thinking, and plan implementation. My manager is relatively flexible and trusts our team on decision making when being on the job. Large issues or concerns are definitely things that are reported to my manager for a second opinion on approaching people trying to fix a continuous problem that they are seeking help in. Our job is finding every person’s weak area of expertise, exposing it to them and recommending various tools and resources that are available to them to help them get better at a certain area. Decision-making might be the most important concept; it will be what fixes the problem...