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I am working on employee attrition analysis with a table having rowwise data for a (employee like Id, name, Date_Join Date_Relieving Dept Role etc)

eID eName Joining Releiving Dept Married Experience
123 John Doe 10Oct15 12Oct16 HR No 12
234 Jen Doee 01jan16 -NA- HR No 11 (ie she is available)

I can run regression on this data to find the beta coefficient

eID eName Joining Releiving Dept Married Experience
123 John Doe 10Oct15 12Oct16 HR No 12
234 Jen Doee 01jan16 -NA- HR No 11

But I've seen other approach too.. where employee have multiple entries depending on their difference between joining date and current month or relieving month(say Employee A joined in Jan and Left in Dec so he'll have 12 entries updating corresponding columns like experience and marriage etc)

eID eName Dept Married Experience
123 John Doe HR No 0
123 John Doe HR No 1
123 John Doe HR Yes 2
123 John Doe HR Yes 3

can someone tell what differentiate two approaches.. and what is the outcome of this second approach.

Makarand
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  • I think you could improve your question making it clearer. Anyway, are you refering to a _multinomial model_? – Rodrigo Remedio Nov 04 '16 at 17:02
  • @RodrigoRemedio Thanks for response man.. i'll try to make it more clearer.. and its not multinomial as dependent is just leave or no_leave.. actually its progression of the independents over time – Makarand Nov 09 '16 at 04:12

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