Home - Answers Community - Algebra Calculator - Student Deals - MBA Guide - Business Degree Guide - College Algebra - Mathematics - Research Statistics - About
Free Homework Help

RES/342 Week 3 Answer Guide - Statistics

ANOVA and Non-Parametric Tests

ANOVA and Non-Parametric Tests

            Praxidike Systems is a software company that is having trouble determining why their projects are not being completed on time. The scenario starts by presenting two non-parametric testing methods with include ANOVA and Kruskal-Wallis. Non-parametric testing methods have certain requirements to be utilized effectively. The major assumptions of ANOVA testing include the following: the population has a normal distribution, errors are independent, and each population has the same variance. The Kruskal-Wallis test, on the other hand, does not require the assumption of a normal distribution and the data must be on an ordinal scale. This is typically a better option if all the assumptions of ANOVA cannot be met. The scenario provides three examples of how these testing methods can be applied to real world situations.

During the first phase of the scenario I chose to apply the Kruskal-Wallis test because all the assumptions of ANOVA could not be met. After analyzing the data, it was apparent that the level of competency of the software engineers was correlated with their productivity. In the second phase I decided to run the two-way ANOVA test that analyzed the difference between competency and project type. The test provided 3 null hypotheses, which led to similar results to those in phase one. Last, these testing methods are used to analyze a situation in which a customer was unhappy because of a product defect. A two-way ANOVA test led to the conclusion that a defect tracking system should be in place to prevent future occurrences. An independent analysis of each group concluded that this would be the most effective solution.

Overall, my suggestion to the company is to provide further training to engineers because greater competency leads to greater productivity. It would also be beneficial to set a plan for each project based on the level of competency needed. A well defined plan will help management more effectively delegated engineering tasks for each project. In addition, the defect tracking system will help the company limit the amount of problems with the finished software. Implementation of these suggestions could increase the quality control and overall efficiency of Praxidike Systems.

Non-parametric and ANOVA testing methods can be applied to a variety of situations in the workplace. For example, take our sales department that is separated into different teams. The variation in performance of each team can be based on many possible factors, such as experience, product knowledge, and management. Each of these testing methods could be applied to analyze with factors have the most impact on the performance of each sales team. This data can provide valuable information to managers, who can use it do make improvements to each sales team. Furthermore, each salesperson is paid on commission, so the results of the tests would also be desired by each individual. In brief, ANOVA and non-parametric testing have a wide range of applications in the business world.

Need More Help? Try our Amazing Statistics Calculator

online Center for Mathmatics Excellence

comments powered by Disqus
Get Final Exam Answers from ACCNerd