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There are missing values in the input variables for a regression application.
Which SAS procedure provides a viable solution?
Suggested Answer: C
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12 comments · newest first
DA
Dante
12 months ago
Hmm, I have my doubts about GLM being the best option here.
DO
Doyle
12 months ago
I didn't know GLM could handle missing data like that!
BE
Bernadine
1 year ago
Wait, are you sure? I thought VARCLUS could help too.
LU
Luther
1 year ago
Totally agree, GLM is the go-to for that!
EM
Emmanuel
1 year ago
I think the right choice is GLM for handling missing values.
QU
Queen
1 year ago
CLUSTER seems more focused on grouping data rather than dealing with missing values, so I doubt it's the answer.
TE
Tegan
1 year ago
I feel like STDI2E could be relevant, but I can't recall its specific function in handling missing data.
AN
Annabelle
1 year ago
I remember practicing with the VARCLUS procedure, but I don't think it directly addresses missing values in regression.
AL
Alva
1 year ago
I think the GLM procedure might be the right choice since it can handle missing values, but I'm not entirely sure.
JA
Jaleesa
1 year ago
This seems like a straightforward question about the responsibilities of a mutual fund's board of directors. I'll carefully read through the options and think about which ones best match the given responsibilities.
CA
Catalina
1 year ago
Okay, I think I've got it. The client tier talks to the application tier, which then talks to the data tier. I'll select the options that match that flow.
AU
Audra
1 year ago
I'm a bit unsure about the "average depth test" part of the question. Does that mean I need to consider some combinations more than others? I'll have to think this through carefully to make sure I don't miss anything.