Anova And Non-Parametric Tests

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Applying Analysis of Variance (ANOVA) and Nonparametric Tests Simulation NR! RES/342 August 15, 2011 Steven Gady Applying Analysis of Variance (ANOVA) and Nonparametric Tests Simulation In this simulation, Praxidike Systems, a software solutions company, needed to determine the cause of the delay of 10% of its projects, low productivity, and low customer satisfaction. The Kruskal-Wallis and ANOVA tests were used to identify problem areas and recommendations for improvement were made based on the findings. With ANOVA, the following assumptions made: the population has a normal distribution; all errors are independent; and population variance is the same. A separate test must be conducted to determine if the population has a normal distribution - the chi-square test checks for goodness of fit. Conversely, the Kruskal-Wallis test does not assume normal distribution, but the data is on an ordinal scale. Initially, due to the inability to meet all the assumptions required by ANOVA, the Kruskal-Wallis test was used to analyze the data which indicated a correlation between levels of competency and productivity. Secondly, I chose ANOVA to analyze the difference between level of competency and project type. The results of this analysis supported the findings from the Kruskal-Wallis test. Additionally, reasons for low customer satisfaction and productivity were identified as a result of these tests. To correct these deficiencies, it is recommended that additional training be made available to directly improve productivity. Implementing a defect tracking system will help identify and correct or eliminate defects in finished products. Also, project managers can more effectively delegate assignments based on competency levels which should help increase productivity and product defects. Overall, increased productivity and defect elimination should directly

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