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Phase 3 Individual Project Christopher Addison Instructor: James Velic MGMT600-1203B-04 Applied Managerial Decision-Making Colorado Technical University Online September 10, 2012 Non-parametric Test Company W is looking at testing some new sales software. Company W’s sales force equals around five-hundred people and is divided into four regions Northeast, Southeast, Central, and West. The sales people are all expected to take the product and all sell the same amount. Only half of the sales representatives received the software in each region to help them manage their contacts. The VP of Sales at WidgeCorp, who is comfortable with statistics, wants to know the possible null and alternative hypotheses for a non-parametric test on this data using the chi-square distribution (CTU 2012). The non-parametric test is used when the data is qualitative and categorical as it refers to gender, age group, region, and color. The test is used when there is no sense in the mean of such variables. This can be done by taking the chi-square distribution factor and using the information that has been provided for us we can figure out how to get our null and alternative hypotheses. Non-parametric test are also referred to as distribution free tests. These tests have the obvious advantage of not requiring the assumption of normality or the assumption of homogeneity of variance They compare medians other than means and, as a result, if the data have one or two outliers, their influence is negated (Unknown 2012). What we will be doing is selecting a sample that is random from the population as a whole with a deviation that equals a with the random sample equaling n. So what this means is that the deviation is going to equal is s. This means using the chi-square there is the equation of x2= [(n – 1) * s2] / 2a. If we took the value of chi square as less than the table

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