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Chi Square Test of Independence |
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What does it do?  | The chi square-test of independence is a test of association (nonindependence) between discrete variables. It is also referred to as the test of association. It is based on a mathematical comparison of the number of observed counts against the expected number of counts to determine if there is a difference in output counts based on the input category. Example: The number of units failing inspection on the first shift is greater than the number of units failing inspection on the second shift. Example: There are fewer defects on the revised application form than there were on the previous application form |
Why Use? 
| The chi square-test of independence is useful for identifying a significant difference between count data for two or more levels of a discrete variable Many statistical problem statements and performance improvement goals are written in terms of reducing DPMO/DPU. The chi square-test of independence applied to before and after data is a way to prove that the DPMO/DPU have actually been reduced |
When Use? 
| When you have discrete Y and X data (nominal data in a table-of-total-counts format, shown in fig. 1) and need to know if the Y output counts differ for two or more subgroup categories (Xs), use the chi square test. If you have raw data (untotaled), you need to form the contingency table. Use Stat > Tables > Cross Tabulation and check the Chisquare analysis box |
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