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.thomp...@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 > >______________________________________________ >R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see >https://stat.ethz.ch/mailman/listinfo/r-help >PLEASE do read the posting guide >http://www.R-project.org/posting-guide.html >and provide commented, minimal, self-contained, reproducible code. -- Sent from my phone. Please excuse my brevity. ______________________________________________ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
