Step 1: Apply A Reductionist Approach
A blog is made of posts so let's look at our history of posts. To make 1000 posts over 3 years, 9 months, and 7 days we averaged a post every 31.5 hours - not bad for a bunch of very busy grad students. If we look at the number of posts over time, we see that the distribution wasn't at all even.
Step 2: Look For Trends
If you take a squinty-eyed look at the history of our posts, you can make up a slight pattern. We seem to post more in the summer and less near the start of fall and the end of spring. If we plot the average number of posts per month over the calendar year, complete with a 1-sigma error region, we see that while there is something of a trend, it's hardly iron-clad.
Step 3: Derive A Metric
We know we're writing posts, but does anybody care? Luckily we can invent a quantity to measure how much discussion in generated by our posts, or the average number of comments per post.
Step 4: Fit To A Model
No piece of data analysis would be complete without some sort of attempt to fit our data to a model. What theoretical model best fits the growth of a blog made by a bunch of nerds from BYU? In a word, growth. Looking at the number of unique IP addresses visiting the blog per month since late 2008, we see nothing but roses.
To all the readers, thanks for putting up with the first 1000 posts. Here's to the next thousand.