Here's a plot that should have been included in
part three of Mining Jobmine. It describes the change in distribution of applications as the length of job posting changes. For each x-value representing a certain length of job posting, the plot gives an estimate on the portion of jobs that has 0 to 22 applications, 23 to 40 applications, 41 to 70 applications and >71 applications. The end points are chosen to be the 25th, 50th and 75th percentiles of the number of applications.
![](https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjuY5Z4u73ZHj273515_phMuXfYB53YjGCvwy837kRZ4SmtBYQYBkdoUDQzvDdFAfpBhjZkLBUdqHB0p6PeBsEVZtvuCZS5ojk_H_qxt3ybdlgzmHYb06pFMeeha0VbIok0bzbMbMubUFE/s400/cdplot_length)
The size of the "0 to 22 applications" category increases steadily as the length of job posting increases from 30-ish to around 500, indicating a drop in application. But as the length of job posting increases beyond 500 words, the size of the bottom-most category decreases. This decrease is offset by an increase in the size of the ">71" category. My guess is that jobs with really long job postings are
ones where multiple positions are advertised (e.g. Google job posting...).
Compared to what I had before, this is a much better way of visualizing the correlation between length of job posting and application.
End of Entry