Why is that when I categorize a variable in logistic regression by making it binary at the 75th percentile cutoff, it makes Variable 2 which was previously significant into non-significant. Then, when I change the categorization to binary while using an outlier number much greater than the 75th percentile as the cut off , Variable 2 then becomes significant again? For example 1) model event1= variable 1(continuous), variable 2(categorical) - variable 1 is significant, variable 2 is significant 2) model event1= variable 1 (categorical at 75th percentile), variable 2(categorical) - variable 1 is significant, variable 2 becomes non-significant 3) model event1= variable1 (categorical at outlier point, much greater than 75th percentile), variable 2(categorical) - variable 1 is significant, variable 2 is again significant
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