Analysis of the numbers of views of my blog part 2

Last time I looked at the number of views per month I was getting on my blog. This time I will go into more detail on the numbers behind this.

First up is the number of views per day for each entry by time. This should give me an indication of which entries are more popular. I've divided the number of views by the number of days since the entry was written so that individual entries can be compared on an equal basis.

The picture is possibly a little hard to see when plotted like this but it does show that there are some entries that have a much higher 'popularity rating than others. To put this in a better perspective I've plotted a frequency distribution of them:

There are roughly four entries/posts that have more than 1 view per day. These are my 'popular' entries (and yes, this is popular in a very relative sense).

Let's look at the titles for the top 20 posts. Is there something in common? If I want to make this blog more popular is there something I should focus on? Something other than statistics probably. Anyway here is the chart:

Well, 2 of the popular posts are Java related, 2 are Excel related. I only have 4 Java related posts so this is fairly good for Java. However I have over 20 Excel related posts - not so good for Excel. Especially as one of those is to do with rim weighting.

My next chart shows the number of times that a post label appears. For those that don't know, the labels are just descriptive words or phrases that describe the post.

You can see that I've been concentrating on Excel quite a bit over the years.

The last chart shows the average views per day for these labels ranked by the average.

This just re-iterates the points above - Java is popular. Having said that, Java also has a very high standard deviation. Only one post has ever been relatively very high. And it was one of the first that I did and was about JavaFX rather than plain old Java.

As for whether I can make the blog more popular by picking what to write about based on these figures, I'm not sure. I think that there is far too much noise in the data. It's hard to pick a common theme. I'm sure I could do more analysis on this data but one for the future I think.