Two Sample Hypothesis

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Nonparametric Hypothesis Testing Paper
Team B
RES 342
Eric Hogan
University of Phoenix

Nonparametric Hypothesis Testing
Nonparametric testing does not depend on certain data in a particular distribution. Also, nonparametric testing applies techniques that do not assume that the basis of a model is predetermined. In a previous paper we discussed a hypothesis with single and double samples. Now we will conduct the equivalent, nonparametric test of the real estate hypothesis using another five-step process. The testing we will use in this paper will be the Wilcoxon Signed-Rank Test. The Wilcoxon Signed-Rank Test compares a single sample median with a benchmark using only ranks of data instead of the original observations. It is used to compare paired observations. An advantage of the Wilcoxon Signed-Rank Test is the freedom from the normality assumption. Other advantages are robustness to outliers and applicability to ordinal data (David P. Doane, 2007). In the Wilcoxon Signed-Rank Test the population should have a lot of similarity. The data should have some correlation like houses and price for example. Our hypothesis is as stated: If a real estate home has 3 bedrooms or more, then the price is at least 200,000 dollars or more. The possible outcomes for the tests are left-tailed, two-tailed and right-tailed. The intention of this assessment is to increase knowledge of respective research.
The Wilcoxon test is a nonparametric test that tested the difference between each set of pairs, and analyzes only the differences between the paired measurements for each subject. The whole point of using the Wilcoxon Signed-Rank test is to control the experimental variability, and therefore increase the power. Factors that don’t have control in the experiment will affect the before and after the measurements equally. Within the housing sector these days is very important that homes are competitively priced, and along with taking into consideration the



References: David P. Doane, L. E. (2007). Applied Statistics. New York, NY: McGraw-Hill Irwin. University of Phoenix. (2007). Applied Statistics in Business and Economics. Retrieved from University of Phoenix, RES342 - Research and Development website.

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