Nonparametric Hypothesis

Kedric Simmons

RES/342 Research and Evaluation

Professor Roussas

15 September 2011

Abstract

In this writing I will apply the analysis of a variance and Nonparametric Test simulation towards the real estate data set. A thorough summary will address the three lessons learned relative to a ANOVA and nonparametric test. It will also detail what concepts and analytic tools can be used in the workplace. Furthermore, the five step process will once again be employed to determine whether to reject or accept the null. However, in this particular experiment the Kruskall-Wallis test will be use to test the hypothesis in step three.

Nonparametric Hypothesis Testing

In a continuation of the previous week’s research I was able to formulate a viable hypothesis statement in a nonparametric approach using a chi-square test. Chi-square, is defined as a statistical test commonly used to compare observed data with data we would expect to obtain according to a specific hypothesis. For example, if, according to Mendel's laws, you expected 10 of 20 offspring from a cross to be male and the actual observed number was 8 males, then you might want to know about the "goodness to fit" between the observed and expected. Were the deviations (differences between observed and expected) the result of chance, or were they due to other factors? How much deviation can occur before you, the investigator, must conclude that something other than chance is at work, causing the observed to differ from the expected? The chi-square test is always testing what scientists call the null hypothesis, which states that there is no significant difference between the expected and observed result.

Based upon the research of the data (quantitative) provided for the value of homes which was based on the sample sale price of $200,000. The Chi test was able to determine the data in a qualitative way; showing if the value of the home sampled were above or below $200,000 given the...