| day_name | day | hour | risk grade | count |
| Friday | 1 | 0 | A2 | 6 |
| Friday | 1 | 0 | A3 | 6 |
| Friday | 1 | 0 | D1 | 3 |
| Friday | 1 | 0 | D2 | 2 |
| Friday | 1 | 0 | DN | 1 |
| Friday | 1 | 0 | DS | 6 |
| Friday | 1 | 1 | A2 | 4 |
| Friday | 1 | 1 | A3 | 4 |
| Friday | 1 | 1 | D1 | 1 |
| Friday | 1 | 1 | DS | 1 |
| Friday | 1 | 2 | A3 | 1 |
Hi, my data consists of hourly application count for each risk grade by day of the month and day of the week. Attached data here.
As the application processors receive these volume of applications and make decisions on them, they are being evaluated by the approval rates etc. Approval rates on A1 are the highest and in general approval rates are higher for A (A1,A2,A3) grade applications. So if I am trying to evaluate application processors performance on approval rates, to be on the fair side, I will have to test first if they receive same frequency of risk grade applciations. For example, if I see higher approval rates for night shift application processos, i need to test to see if they receive more A grades compared to the day shift processors, similarly for weekday processors vs weekend processors, and internet applications vs phone applications etc. To visualize the relation between risk grade volume and hour, I can do a simple 2*2 freq matrix and see if the distribution manually, but as the dimensions/slices to the data increase, riskgrade*day_of_week*hour or riskgrade*day_of_month*day_of_week*hour etc.. if becomes cumbersome to look at the data manually. Is there any procedure that I could use to solve this? I have tried using PROC CORRESP, but not sure if that is the appropriate procedure to use here. Please suggest me which statistical method or procedure to find the association between these variables. Thanks.
