Minitab Multiple Linear Regression Help
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 This topic has 4 replies, 3 voices, and was last updated 1 year, 7 months ago by Robert Butler.

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May 4, 2021 at 4:18 am #253669
shar6580Participant@shar6580 Include @shar6580 in your post and this person will
be notified via email.I am aiming to assess the effect of BMI (continuous) on certain biomarkers (also continuous) whilst adjusting for several relevant variables (mixed categorical and continuous) using multiple regression. My data is nonnormal which I believe violates one of the key assumptions of multiple linear regression. Whilst I think it can still be performed I think it affects significance testing which is an issue for me. I think I can transform the data and then perform regression but I’m not sure and also have some questions regarding the implications of this. I have tried Boxcox transformation but Minitab is unable to do this as some of the values are zero. I have performed Johnson transformation (which I think is a variation of YeoJohnson transformation) and now have columns of normal looking transformed data but – A) is this the right thing to do, B) if so do I need to do this for all nonnormal variables or just the outcome variable, C) how will this effect the beta coefficient in terms of calculating the final quantitative effect BMI has on the variable given that I’m using transformed data and not the original? I could potentially change my outcome variable to a categorical variety and use multiple logistic regression but not sure if/how this helps. I am not thinking about nonlinear regression at this point as this seems more complex (perhaps too much for me) and I’m hoping to solve this issues without in the first instance. Any help would be much appreciated.
0May 4, 2021 at 2:04 pm #253686
Ken FeldmanParticipant@Darth Include @Darth in your post and this person will
be notified via email.@shar6580 Double check the correctness of your assumptions. The only assumption for normality is in the residuals. There is no normality assumption for the raw data. Save your efforts of all those transformations.
0May 4, 2021 at 2:31 pm #253687
Robert ButlerParticipant@rbutler Include @rbutler in your post and this person will
be notified via email.Darth is correct. Here’s the relevant quote from a standard statistics text.
Applied Regression Analysis 2nd Edition – Draper and Smith pages 22 and 23
“[with regard to regression] Up to this point we have made no assumptions at all that involve probability distributions. A number of specified algebraic calculations have been made and that is all. We now make the basic assumptions that for a model of Y = fn(Xi +e) (I can’t write epsilon using this platform so I’m using “e” in its place)
1. e is a random variable with mean zero and variance sigma**2.
2. e(i) and e(j) are uncorrelated such that cov(e(i),e(j)) = 0
3. e(i) is a normally distributed random variable, with mean 0 and variance sigma**2″There are no other assumptions/requirements.
The need for approximate normality in the residuals is because it is the residuals that inform the correctness of the t and F tests used for assessing term significance. In order to address the issue of approximate normality you will need to run a residual analysis – and when you do please follow the guidelines for running a proper analysis (Chapter 3 of the above book has the details) this means assessing the residuals graphically – not just dumping them into some test for normality.
0May 4, 2021 at 2:48 pm #253688
Ken FeldmanParticipant@Darth Include @Darth in your post and this person will
be notified via email.@rbutler Robert, as usual, a concise simple to understand explanation LOL. I always learn so much from your posts…at least the parts that I understand. Hope all is well.
0May 4, 2021 at 5:09 pm #253690
Robert ButlerParticipant@rbutler Include @rbutler in your post and this person will
be notified via email.@Darth – things are going well here – how about you and yours?
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