Timeline for Should I ever standardise/normalise the target data/ dependent variables in regression models?
Current License: CC BY-SA 3.0
8 events
| when toggle format | what | by | license | comment | |
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| May 18, 2021 at 15:10 | comment | added | Carl | It only makes sense to normalize your data if it is normally distributed in the first place. If the distribution of your explanatory variables cannot be made to be normal, then modelling them as such will be incorrect and you will likely wind up with z-scores that are very large in magnitude and have poor explanatory power. | |
| Jan 6, 2016 at 0:27 | comment | added | Glen_b | The coefficients in a regression model take care of differences in scale. (There are sometimes reasons to standardize but from your description you don't seem to be in need of it.) | |
| Jan 6, 2016 at 0:24 | vote | accept | SARose | ||
| Jan 5, 2016 at 23:11 | answer | added | Matt L. | timeline score: 10 | |
| Jan 5, 2016 at 22:27 | comment | added | EdM | Is there a reason why you want/need to standardize the dependent variable? Then your dependent variable, rather than being in a natural scale like "meters," is in a scale with units of the standard deviation found in your particular sample. Similarly for the explanatory variables. Staying in the original scales can help with interpretation and with the ability of others to reproduce your results. | |
| Jan 5, 2016 at 21:53 | answer | added | imonaboat | timeline score: 1 | |
| Jan 5, 2016 at 20:51 | review | First posts | |||
| Jan 5, 2016 at 20:58 | |||||
| Jan 5, 2016 at 20:47 | history | asked | SARose | CC BY-SA 3.0 |