My name is Abhi and I am fairly new to statistics. I found some sample exercises online & I am trying to solve them to get a better understanding of model development.
Assume a forum like stack exchange or cross validated. Using parameters like number of comments, number of upvotes, reputation points of the user, etc (there are about 10 more fields) can you predict the likelihood of the question getting answered. There about 4000 records to consider
It seems to me that number of up votes and comments should be strong indicators. However when I graph them (and do the chi square significance test) I get very poor results
number of up votes - p value much less than 0.05
number of comments - p value much less than 0.05
What should be my next step from here? Is there something obvious that I am missing? Are there any transformations that I should consider. Any help would be much appreciated
I graphed the distribution for number of comments & number of likes. They are not normally distributed
Based on the suggestions below, I calculated the means and standard deviation of the 2 groups - number of comments for the people whose question was answered (m-2.094,sd-4.008) & number of comments for people whose question was unanswered (m-5.22,sd-5.688). Both of them are within 1 standard deviation of each other. I ran the t test and the difference between the 2 means is 1.91 with a p value <<< 0.0001 Does this mean this feature is useless or do I need to transform this feature