I still need a lover with an alibi
So I'm trying to turn a giant stack of data into something meaningful. It's about how long it takes to promote people, how long it's been since a promotion when people leave, what the current population looks like in terms of time since promotion. And whether all of that differs by gender and race - that's the big point to it. So I have three datasets: a list of promotions with time between promotions, a list of terminations with time between last promotion and termination, and a list of currently active employees with time since last promotion. What would have been great to have is a list of active employees with that info as of several years ago as well, but the database is really limited. Anyway, I have some ideas for how to do this, but if any of you have any brilliant ideas or questions you would want to see, I'm all ears. The sort of question we're trying to answer is whether we're only promoting white dudes, whether people are leaving because only white dudes are promoted, etc. If I have time, I may look at age groups as well. But, good grief, to really do this absolutely right is like a term paper, not something you whip together in a week while you're busy doing a bunch of other stuff because $UBER_BOSS is presenting a bunch of your group's work to Chief Something or Other Officer.
The results are guaranteed to be inconclusive, but suggestive.
So, I was watching a show last night and the power went out on the whole block. I was pissed. There wasn't anything going on, weather-wise, to make it comprehensible. The day before had been windy and stormy. Last night, however, nothing was going on. The power was out for a little over an hour, but it was too late to finish off the show.
I just thought about it: I haven't been getting the reward certificates from my Amazon.com visa. Whatte the swyve?
I'm updating my resume. I'm sending it out to a couple internal postings - one is in Texas and one is here, but a step above what I could get into. However, it has also been open for three months.
And, you know, the thing is, if they're different by .15 of a year, is that significant? Does it make a difference? Suppose it is statistically robust - should we care?
Okay. That's enough of that.