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I have two independent continuous variables like Age, Price and an outcome variable like purchased or not purchased

Now, which test should I use to ascertain the association of continuous input variable with categorical outcome variable?

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  • $\begingroup$ isn't like a classic differential test ? if you have normalized distributed feature one can use t-test, otherwise sth like wilcoxon - the result will be whether the continues value is enriched in one group that the other ?! $\endgroup$
    – user702846
    May 25 at 20:30
  • $\begingroup$ Can't we use point biseral corelation? $\endgroup$
    – The Great
    May 26 at 0:07
  • $\begingroup$ your question is more statistical - I recommend to consult in stats.stackexchange.com - meanwhile : correlation coefficients or coefficients from logistic regression only give you the "effect size" not the level of significance. you have used term : "association" which implies you are after finding the level of significance :-) in stat words matter. $\endgroup$
    – user702846
    May 26 at 7:38
  • $\begingroup$ @user702846 - let's I have one binary variable and continuous variable but the values for continuous variable are only very few. Meaning, ex: in the whole column, only 5 values keep repeating for continuous variable. In this case, can I consider them as categorical (because oy 5 unique values/levels) and apply chi-square test for association? $\endgroup$
    – The Great
    Jul 4 at 3:28
  • $\begingroup$ technically you can - but what it means and how to interpret I don't know. $\endgroup$
    – user702846
    Jul 4 at 20:46
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Try logistic regression. The sigmoid function and its variants allow for conversion of continuous inputs into a binary outcome variable.

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  • $\begingroup$ Can log reg work fpr just 1 input variable and 1 outpit variable? $\endgroup$
    – The Great
    May 25 at 23:56
  • $\begingroup$ cant we use point biseral corelation? Upvoted for thehelp $\endgroup$
    – The Great
    May 26 at 0:08
  • $\begingroup$ Perhaps yes, but also try out f-classif, mutual-info-classif under sklearn.feature_selection $\endgroup$ May 26 at 6:45

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