Chi Square

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Chi square test for independence of two attributes. Suppose N observations are considered and classified according two characteristics say A and B. We may be interested to test whether the two characteristics are independent. In such a case, we can use Chi square test for independence of two attributes. The example considered above testing for independence of success in the English test vis a vis immigrant status is a case fit for analysis using this test.

This lesson explains how to conduct a chi-square test for independence. The test is applied when you have two categorical variables from a single population. It is used to determine whether there is a significant association between the two variables. For example, in an election survey, voters might be classified by gender (male or female) and voting preference (Democrat, Republican, or Independent). We could use a chi-square test for independence to determine whether gender is related to voting preference. The sample problem at the end of the lesson considers this example. When to Use Chi-Square Test for Independence

The test procedure described in this lesson is appropriate when the following conditions are met: * The sampling method is simple random sampling.
* Each population is at least 10 times as large as its respective sample. * The variables under study are each categorical.
* If sample data are displayed in a contingency table, the expected frequency count for each cell of the table is at least 5. This approach consists of four steps: (1) state the hypotheses, (2) formulate an analysis plan, (3) analyze sample data, and (4) interpret results. State the Hypotheses

Suppose that Variable A has r levels, and Variable B has c levels. The null hypothesis states that knowing the level of Variable A does not help you predict the level of Variable B. That is, the variables are independent. H0: Variable A and Variable B are independent. 
Ha: Variable A and Variable B are not...
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