Use a chi-square goodness of fit analysis to test whether an experimental profile is the same as or different than a baseline profile. For example, is the current accounts payable aging profile the same as or different than the accounts payable historical profile? The input data from the process must describe two separate profiles.
The results tell you whether the profiles are different. You must look at the raw data to determine the location of any differences and whether any observed differences are "good" or "bad."
For more information, go to Insert an analysis capture tool.
Use a chi-square test of independence to assess the observed differences in the rates of occurrence for a categorical output at different levels (settings) of an input. To use this test, the data for both variables (input and output) must be discrete or categorical. For example, X could be five different named hospitals and Y could be the likelihood of recovery (high, moderate, low, or unlikely).
When to Use | Purpose |
---|---|
Mid-project | Fixing an input at two or more different settings (levels) helps to determine which inputs have significant influence on the output profile (% by category). |
Mid-project | Verify changes to inputs result in significant differences from the pre-project output profile. |
Your data must be a table containing the counts of each combination of the categorical X and Y values.
Hospital | |||
Chance of Recovery | A | B | C |
Good | 78 | 45 | 98 |
Moderate | 45 | 57 | 55 |
Poor | 44 | 68 | 25 |
For more information, go to Insert an analysis capture tool.