![]() To learn more about Chi-Square Tests, register for Black Belt Training and review Analyze Phase, Module 4.2.3. Go to Stat > Tables > Cross Tabulation and Chi-Square: How To Run A Chi-Square Test In Minitab 1. Learn more about Chi-Square Tests in Analyze Phase, Module 4.2.3 of Black Belt Training. An example would be: Assembly Line A produced 462 good parts and 265 defective parts whereas Assembly Line B produced 538 good parts and 321 defectives and you want to determine if Line A is truly better or the difference is just due to random chance. This test is performed on count data from different samples. It is useful for determining whether or not improvement implementations have been successful. Therefore, the two types of means are identical for balanced designs but can be different for unbalanced designs. Data means are the raw response variable means for each factor level combination whereas fitted means use least squares to predict the mean response values of a balanced design. The Chi-Square Test is a hypothesis test that determines whether a statistically significant difference (aka variance) exists between two or more independent groups of discrete data, ruling out chance. Comparison of data means and fitted means. ![]()
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