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If the statistical test gives a $p$ value < 0.05 can I declare that the original variable is statistically significant between conditions? No. Assuming you are using a linear regression model, and you log-transformed the predictor (although the same applies when you log-transformed the response): the $p$ value tests the null hypothesis of no linear ...


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If you do the equality of variance test for the purposes of assumptions of manova, you need to test the data on the scale you will use in the manova. Presumably (after what you told us) that is the square root scale.


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It is hard for non-R users to hack through that output to see what is going on and possibly not trivial even for R users. Focusing on fromit and total alone I note first very small samples (implying some greater caution in doing anything even a little complicated) but also approximate symmetry. This Stata output may not be exactly what you get in other ...


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Your notebook link was broken, so I couldn't check if the author pointed out a reason to implement log transformation. But, one typical use case of log-transform is with skewed distributions. So, taking the logarithm makes them a bit algorithm (and numerical) friendly. This one (FE column, which seems to be the target variable) seems a bit skewed towards ...


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Any transformation of say 0 and 1 to any two distinct values would change the coefficient estimates but could not improve model fit. Think of this in geometric terms: imagine a plot of your outcome against a binary predictor coded 0 and 1. Changing 0 and 1 to any other pair of values is just re-labelling points on the predictor axis. The most you could do is ...


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