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Res 342 Week 3

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Applying ANOVA and Nonparametric Tests Simulation

In this week’s simulation, I chose the Kruskal-Wallis test. The three lessons learned relative to ANOVA and Nonparametric Tests were the errors are random and independent of each other, each population has a normal distribution, and all the populations have the same variance. From the lessons learned through the ANOVA and Nonparametric Tests Simulation, I will be able to apply the concepts and analytical tools learned at my workplace by applying ANOVA and various nonparametric tests to analyze the results of data for more efficient and effective operations within my organization. Some of the suggestions that I made in the simulation for month one were to conduct a Kruskal-Wallis Test, reject the null hypothesis, and provide training for increase competency, which will help increase productivity of the software engineers of Praxidike Systems. For month two, I selected the type of project and scope changes as factors for analysis. The correlation matrix showed that the factor “type of project” has a strong positive correlation with the productivity of software engineers. The factor “scope changes,” on the other hand, has a correlation coefficient of 0.6. It was difficult to make any definite conclusions from this number as it was more than 0.5, but less than 0.75. Thus, further investigation of the factor is appropriate. I also made suggestions to set competency levels for a project depending on the skill requirements and to review project plans and account for scope changes at the planning stages. For month three, I selected the number of defects as a factor of analysis. This factor has a strong correlation with the variable client satisfaction. The other factors, on the other hand, have correlation coefficients that are less than 0.5. This indicates that the factors do not have a strong positive or strong negative correlation with client satisfaction. Thus, further investigation of the factors is not necessary. I also chose two-way ANOVA to find out if there was any interaction between schedule variance and number of defects. For example, a project that has a high number of defects may also have a high schedule variance and hence may affect client satisfaction. The suggestions that I made were to implement a defect tracking system and to review testing procedures. In conclusion, at Praxidike Systems, I used ANOVA and nonparametric tests to analyze the effect of different factors on two response variables productivity and client satisfaction. The results of the analysis helped pinpoint areas that needed improvement. The analysis helped me manage “Quality.” The additional information that I would recommend to a key decision maker is that one can use statistical analysis to identify process changes and improvements to address quality issues that can otherwise be difficult to handle when they arise
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