Assume company X has a cyber security incident team. One of their key performance/risk indicators is how fast they triage the security incidents. For the past 2 quarters, COO observed decreased amounts of incidents triaged within ideal/expected triage time.
Find out why there has been a decreased amounts of incidents triaged within ideal triage time.
- Employees come and go. There has been a continuous resignations. Test if decreased number of team members has correlation with triage time.
I'm using simple linear regression. Triage time (in minutes) is response variable (y) and 'number of people joined/left' is explanatory variable.
Triage time data range is from 0~940000 minutes, and number of people is only from -4 to 2. -4 being when team lost 4 people and 2 being where they had 2 extra members from where they began the team.
Below is what it looks like (fake data), and I have 65000+ rows
The result is saying the model isn't significant.
Multiple R 0.004008821
R Square 1.60706E-05
Adjusted R Square 8.26758E-07
Standard Error 642.2262936
Significance F 0.30453744
I think maybe there is a better way to study relationship between employee attrition versus triage time.
Is it okay to compare the variables that one has much wider range (y) compare to another (x)? I'm thinking if I was supposed to perform any data transformation so I can somehow emphasize the employee attrition. Do you see anything wrong with the way I design the linear regression?