Education is even more important than ever today for anyone interested in entering the world of employment with either large international corporations, or even local vendors serving the community within the area where one lives. In an ongoing effort by our research team to determine if the difference in the wages from our sample population of men and women, who have various levels of education, does in fact make the difference. We are looking to use an additional test to discover whether or not we can inconclusively state that from our previous test conclusion that our team believes that there is a difference in the wages of the sample population. In our investigation, the team will hope to convince the audience of the hypothesis chosen by the team through introducing our statement regarding the research issue, performing the five step hypothesis testing procedure on the data, explain the nonparametric test the team chose to analyze the data and why we chose this particular test. We will then interpret the results of the test; explain the differences that were observed from the teams week three paper. We have also included the raw data tables and results of this weeks test in Appendix A-D. The key is using the right data at the beginning to make the difference in how the test results will turn out. Data
The data chosen by the team used in this research paper is the same as what was used for the previous two or more sample hypothesis running the One Factor ANOVA test (Doane & Seward, 2007), called Wages and Wage Earners Data Set. To help the team determine the significance between wages earned by men and women of different educational levels, the team needed to convert the data from the tabular format as seen in Appendix A, to a layout of merged data that would assist the team in setting up the nonparametric test as shown in Appendix B. Because the test chosen uses the sum of the rank and the sample size to compare the independent data...
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