Hello,
I am a bit confused about when to use proc reg and when to use proc glm. I want to find out the factors effecting Exists.
Below is my data table
Bounces | Exits | Continent | Sourcegroup | Timeinpage | Uniquepageviews | Visits | BouncesNew |
0 | 0 | OC | (direct) | 18 | 1 | 0 | 0 |
0 | 0 | N.America | (direct) | 4 | 1 | 0 | 0 |
0 | 0 | N.America | Others | 35 | 1 | 0 | 0 |
0 | 0 | N.America | public.tableausoftware.com | 70 | 1 | 0 | 0 |
0 | 0 | N.America | public.tableausoftware.com | 81 | 1 | 0 | 0 |
0 | 0 | N.America | public.tableausoftware.com | 75 | 1 | 0 | 0 |
0 | 0 | N.America | public.tableausoftware.com | 186 | 1 | 0 | 0 |
0 | 0 | N.America | (direct) | 710 | 1 | 0 | 0 |
0 | 0 | OC | (direct) | 712 | 1 | 1 | 0 |
0 | 0 | AS | Others | 344 | 1 | 1 | 0 |
0 | 0 | EU | Others | 27 | 1 | 1 | 0 |
0 | 0 | EU | visualisingdata.com | 0 | 1 | 1 | 0 |
0 | 0 | N.America | Others | 294 | 1 | 1 | 0 |
0 | 0 | N.America | public.tableausoftware.com | 111 | 1 | 1 | 0 |
0 | 0 | SA | (direct) | 1430 | 1 | 1 | 0 |
0 | 0 | N.America | (direct) | 29 | 1 | 1 | 0 |
0 | 0 | N.America | Others | 637 | 1 | 1 | 0 |
0 | 0 | N.America | 10 | 1 | 1 | 0 | |
0 | 0 | N.America | 42 | 2 | 1 | 0 |
Thanks and regards,
Aditya
What values does exits take on? 0/1?
PROC GLM does most of what PROC REG does. There was a comparison a short while ago here:
If your variable takes on the values 0/1 you shouldn't be using PROC REG - use PROC LOGISTIC or GLM. Since you have mostly categorical data, you can also do as already suggested by @Ksharp and use CATMOD.
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proc reg only is suited for continuous variable. proc glm can handle both continuous and category variables. If EXISTS has only two value 0 and 1, then Logistic Regression (proc logistic , proc catmod .... ) or decision tree (proc hpsplit) is a good choice .
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