Using Data to Estimate When My YouTube Channel Will Be Monetized

I don’t know about you, but I love data. But not just plain data without a purpose, instead I like to use data to look at trends and make predictions.

So, not too long ago I decided to start a second YouTube channel — Physics Explained. This channel is basically all of my physics explanations and problems. I started off with stuff for introductory-level physics, but I’ve found that more advanced physics (like classical mechanics) is both fun and popular. Here, check out a video.

But here’s the deal. In order to be able to monetize your channel (put ads on there so that you can get paid) YouTube has 2 requirements: 1000 subscribers and 4000 watched hours of video. Right now, I’m just waiting to get to 4000 hours. This is where the data comes into the game.

In July, I started recording the daily number of subscribers and total watched-hours. Here are some of my ideas about this data.

• I suspect that the watched-hours will be non-linear. At the very least, as I make more and more videos that should lead to an increased rate of watching — right?
• There should be some type of relationship between subscribers and the rate of watched-hour increase…