In this example, an F-test analysis is performed on the fictitious banking data to analyze income by years_with_bank and marital_status.
Parameterize an F-Test analysis as follows:
- Available Tables — twm_customer
- Column of Interest — income
- First Column — years_with_bank
- Second Column — marital_status
Threshold Probability — 0.05
First Column Values — 0, 1, 2, 3, 4, 5, 6, 7
Second Column Values — 1, 2, 3, 4
- Run the analysis.
Click Results when it completes.
For this example, the F-Test analysis generated the following page. The F-Test was computed on income over years_with_bank and marital_status.
The test shows whether significant differences exist in income for years_with_bank by marital_status. The first column, years_with_bank, is represented by F1. The second column, marital_status, is represented by F2. The interaction term is F12.
A ‘p’ means the difference was significant, and an ‘a’ means it was not significant. If the field is null, it indicates there was insufficient data for the test. The SQL is available for viewing but not listed below.
The results show that there are no significant differences in income for different values of years_with_bank or the interaction term for years_with_bank and marital_status. There was a highly significant (p˂0.001) difference in income for different values of marital status. The overall model difference was significant at a level better than 0.001.
F-Test (Two-way Unequal Cell Count) (Part 1) DF Fmodel DFErr DF_1 F1 DF_2 F2 DF_12 F12 31 3.76 631 7 0.93 3 29.02 21 1.09 F-Test (Two-way Unequal Cell Count) (Part 2) Fmodel_PValue Fmodel_PText Fmodel_CallP_0.05 F1_PValue F1_PText F1_CallP_0.05 0.001 <0.001 p 0.25 >0.25 a F-Test (Two-way Unequal Cell Count) (Part 3) F2_PValue F2_PText F2_CallP_0.05 F12_PValue F12_PText F12_CallP_0.05 0.001 <0.001 p 0.25 >0.25 a