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  #491  
Old 04-02-2019, 08:54 AM
Alanbb Alanbb is offline
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That's what I did. Or at least I used excel to help determine patterns and potential errors and then just used R to remove/modify them.

I believe it explicitly says in the syllabus/modules it can be done in excel before importing into R.
How did I miss this? I would have been gutted if I had failed the exam.
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  #492  
Old 04-03-2019, 12:29 AM
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Originally Posted by jts75 View Post
That's what I did. Or at least I used excel to help determine patterns and potential errors and then just used R to remove/modify them.

I believe it explicitly says in the syllabus/modules it can be done in excel before importing into R.
It does. That is why I asked in the first place. In theory it is better to do everything in R but I think getting it done using Excel and R is better than not getting it done.
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  #493  
Old 04-03-2019, 09:40 PM
hkaktwan hkaktwan is offline
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Hi, Anybody having issue accessing the SOA elearning modules for PA exam. Somehow, it keeps clocking for me. Having this issue from 3 days now.
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  #494  
Old 04-04-2019, 12:59 PM
hkaktwan hkaktwan is offline
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Hi, Anybody having issue accessing the SOA elearning modules for PA exam. Somehow, it keeps clocking for me. Having this issue from 3 days now.
I got it resolved through SOA help desk.
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  #495  
Old 04-04-2019, 10:57 PM
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pyramidos pyramidos is offline
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Default M6, end of module question

1. When the answer writer for Rmd 6.8 is trying to figure-out which variables to get rid of, and they aren't purely looking at the AIC (for convergence reasons), then they are taking out variables with high p-values. But a couple of times (like leading-up to Chunk #18), they take out SAGE instead of AGE, even though AGE has a higher p-value. Running Chunks 18, (19), and 20 yields a final model with AGE having p-value of 0.398 and the rmse = 764k, but if you change the AGE to SAGE instead, then the p-value of SAGE is 0.121 in the model and the rmse = 742k (which seems to imply a better model).

And the answer writer is not a priori convinced of never taking AGE out of the model, because just before Chunk #13, they try taking out AGE; but as it happens, the process doesn't converge and so they end-up including AGE.

2. I had assumed that one would never settle on a model where any of the betas has a p-value greater than 0.05 (aside from when two variables need each other as in the MARSTAT with AGEDiff), but the writer does keep high p-value betas. Like NUMHH in the results from Chungus #18 is at 0.382, which means that the beta for NUMHH could easily be negative instead of positive. And given that the objective of this end of module 6 example is to "assist marketing in identifying important factors", telling marketing that there's a positive beta when it could be negative is not helpful.
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Last edited by pyramidos; 04-05-2019 at 10:31 AM..
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