I'm working on a statistics project that includes data for people who post a certain project online they need another to complete. Many people will apply to work on the project, but the poster will only choose one to complete it. There is a "back and forth" between the two parties until the project is completed. My job is to find all the projects that haven't been completed and to figure out whether they will ever be completed (ie the poster doesn't care about finishing the project, but it's still posted in the system - we want it gone).
I've started by analyzing the data for "completed" projects to later compare to "not yet completed" projects. There are areas such as "how long to post a project after signing up", "how long to get a first response to a project", "how many applications until the poster makes a selection," etc.
Here is a histogram of what each of the data columns looks like. Each seems to require a log transformation. (Ex: x is number of days to respond).
My main question is this:
Am I approaching this the correct way? Should I find the mean for and standard deviation for a variable such as "days to get a response" (from the "completed" data) and then if there's a value outside two standard deviations from the "not yet completed" data assume that project will not be completed?
Or should I group all the variables together and do a multivariate analysis and compare the Mahalanobis distance of the "not yet completed" group?
My problem with the multivariate approach, however, is that it's hard to think about the "ellipse around the data" (because it seems to be bounded by a square). Below is a plot variables against each other with their log transforms.
Or should I look at correlations?
Any help with how to approach would be much appreciated. Thanks.