# Goodness of Fit Test And Test For Independence In Chi-Square Distribution

### Grand Canyon – Sym 506 Weekly Discussion

#### How many different tests does the textbook give you for applying the chi-square distribution? What are these tests? How could you use each of these tests at your place of business?

Chi-square distribution is a mathematical or theoretical distribution that has an extensive applicability in the work of statistics. The minimal probable value of chi-square is 0 though there are no minimal values. Every Chi-square distribution contains a level of freedom related with it, such that there are a number of chi-squared distributions. Chi-square distribution contains a number of tests based on the textbook. These tests include goodness of fit test, test for independence. The goodness of fit test starts by hypothesizing that the variable distribution behaves in a certain manner. For example, to be able to evaluate the needs for daily staffing in a business, the manager may consider evaluating the daily flow of customers in the retail store. In this case, the manager will create a hypothesis which will presume equal number of customers in every day of the week. This would be considered as a null hypothesis. This hypothesis will then be tested by conducting a research. Chi-square computation will then be carried out using all the data collected in every day research reading and a justification of the hypothesis evaluated. The chi-square test for two variables independence is a test that employs a cross classification table to evaluate the relationship nature between the variables. The tables are frequently known as contingency tables. The tables demonstrate the way in which two variables are either not associated or how they are associated with each other. The test of independence, similar to goodness of fit test is quite general, and can be employed with variable evaluated on any form of scale, ratio, nominal, interval or ordinal. Test of independence is employed by presuming that there is no association between the two variables being evaluated. For instance, in business, the manager would presume that there is no relation between the workers motivation and the product quality and this would be tested in a research.