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04-29-2017 09:46 AM

Hello

1) In Multiple Linear Regression what is the relation between beta coefficients and errors. Can we say if errors are normal distributed then beta coeffients are also normally distributed?.

2) Why do we need to look for convergence of logistic regression model, when can we say the model doesn't converge. Please refers me a book or simple example.

3) Request you to suggest me books on Dimension Reduction Techniques which includes topics like Factor Analysis, Principal Component analysis and Descriminent analysis.

Thanks

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05-15-2017
04:58 AM

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04-29-2017 10:40 PM

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05-15-2017
04:58 AM

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04-29-2017 10:40 PM

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05-01-2017 03:33 PM

This is from way back in the reptialian part of my memory, but ...

First, Beta is the underlying coefficient. It's not distributed. But BetaHat (estimation of Beta) is distributed.

Now If

- Y = Beta'X + e, and
- error term
is normally distributed**e** - BetaHat estimation is BetaHat = Inverse(X'X) (X'Y) [=Inverse(X'X)*Beta'X + Inverse(X'X)e]

then under closure of normal distributions under linear transformation, BetaHat must be normally distributed.