The first compares aging (0-500 days) per unit in inventory as the dependent variable and product type (type 1-5) as the independent variable. My results are:

Where P1-5 are product 1, product 2, etc.

From this I can see that the sample means vary a good amount, the F ratio of 108.38 is rather large (which is evidence I think that these independent variables have an impact on aging, and at the bottom you can see that P2-P4 and P3-P5 have similar 'aging' times but the rest vary a good amount.

The second comparison is aging (same data as above) vs the independent variable of 10 sales regions.

^the confidence interval tests is cut off in the screenshot.

My main questions are:

1.) For the second test, the F ratio of 26.4 and the low p value seems to indicate that the aging is influenced somewhat by what sales region it is in, right?

2.) The first test of product vs aging has a F ratio of 108.38, the second test of sales region vs aging has a F ratio of 26.4. Am I able to say that (since both have the same dependent variable), the larger f ratio of product has MORE of an impact on aging than sales region, although both clearly impact aging?

Thanks so much! If you have any other random comments on how to interpret my data please let me know.

FYI my data looks like this, where each row is a unit in inventory:

But to do the ANOVA, I separated it into two tables where one is product + aging, and the other is sales region + aging.