Applying analysis of Variance (ANOVA) and Nonparametric Tests Simulation NR! RES/342 princely 15, 2011 Steven Gady Applying Analysis of Variance (ANOVA) and Nonparametric Tests Simulation In this simulation, Praxidike Systems, a software solutions company, require to determine the cause of the delay of 10% of its renders, abject growthivity, and low guest satisfaction. The Kruskal-W allis and ANOVA tests were utilize to let on problem areas and recommendations for return were made ground on the findings. With ANOVA, the interest assumptions made: the population has a prescript scattering; all errors are commutative; and population variance is the same. A identify test moldiness 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 non assume normal distribution, but the data is on an ordinal number 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 correlativity between trains of qualification and productivity. Secondly, I chose ANOVA to analyze the variance between level of competency and project type. The aftermaths of this analysis back up the findings from the Kruskal-Wallis test.

Additionally, reasons for low customer satisfaction and productivity were identified as a result of these tests. To regenerate these deficiencies, it is recommended that additional training be made usable to directly improve productivity. Implementi ng a flaw tracking placement will help ide! ntify and correct or press out defects in finished products. Also, project managers can more in effect delegate assignments based on competency levels which should help add productivity and product defects. Overall, add-ond productivity and defect emptying should directly increase customer satisfaction. The goal of continuous overture can be achieved as a result of...If you deficiency to get a full essay, order it on our website:
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