[R] Scaling - does it get any better results than not scaling?

[Jeff Newmiller]

This question is interesting, but sadly off-topic here as there is nothing specific to R in it. Fortunately there are many resources for getting an answer... e.g. a quick search with Google finds [1] which addresses both centering and scaling.

[1] https://stats.stackexchange.com/questions/29781/when-conducting-multiple-regression-when-should-you-center-your-predictor-varia

On July 16, 2018 9:53:17 PM PDT, Michael Thompson <michael.thompson using manukau.ac.nz> wrote:
>Hi,
>I seem to remember from classes that one effect of scaling /
>standardising data was to get better results in any analysis. But what
>I'm seeing when I study various explanations on scaling is that we get
>exactly the same results, just that when we look at standardised data
>it's easier to see proportionate effects.
>This is all very well for the data scientist to further investigate,
>but from a practical point of view, (especially IF it doesn't improve
>the accuracy of the result) surely it adds complication to 'telling the
>story'
>of the model to non-DS people?
>So, is scaling a technique for the DS to use to find effects, while
>eventually delivering a non-scaled version to the users?
>I'd like to be able to give the true story to my students, not some
>fairy story based on my misunderstanding. Hope you can help with this.
>Michael
>
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-- 
Sent from my phone. Please excuse my brevity.