Timeline for Model comparison: with raw or normalized data?
Current License: CC BY-SA 4.0
6 events
| when toggle format | what | by | license | comment | |
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| Jan 30 at 22:03 | history | edited | CaroZ | CC BY-SA 4.0 |
added 593 characters in body
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| Jan 30 at 21:08 | history | edited | CaroZ | CC BY-SA 4.0 |
fixed a typo
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| Jan 30 at 14:47 | comment | added | M.S. | Do you mean that I could use the strength of the correlation between Log10(Literature_var) and Index_normalized as a point to statistically validate my Index_normalized? Thank you for your input | |
| Jan 30 at 14:39 | comment | added | CaroZ | Still, you should know whether they are at least correlated. | |
| Jan 30 at 14:36 | comment | added | M.S. | Actually, I am not interested in exploring the correlation with Log10(Literature_var), I am interested in finding statistical evidence for my Index_normalized, where for evidence I mean the AIC of the corresponding model is lower than that of the model with Log10(Literature_var). I obtained this but the problem is that if I compare Log10(Literature_var) with the raw values of the index I get the opposite (independently from the correlation). So I am not sure if when I am affirming that my Index_normalized is a good predictor I am mistaken because I should compare the raw values instead. | |
| Jan 30 at 14:28 | history | answered | CaroZ | CC BY-SA 4.0 |