High school growrth, 1990-2007

After yesterday's scatterplots of college and graduate school growth, I thought that for the sake of completeness, I should look at the same figures for high school. I was assuming I would have a pretty similar scatterplot.

And I was mostly right, although the correlation is less defined here, and there is a significant group of outliers. Also, much as with the college and graduate school graphs, this is a pretty good repudiation of the "Saturday Night Live syndrome" about US education---Americans are more educated than they were in 1990. (Although, of course, someone can always "prove" via an e-Mail forward that students in the 1950s all learned calculus and Latin in 8th grade, so our educational system was stronger then).
The rate of high school graduation increase varied from 4% in Alaska, to 24% in Kentucky. Which would seem to be bad news for Alaska, besides that Kentucky's numbers are still below what Alaska's were in 1990. The greatest growth was in the southern and Appalachian states that were the furthest behind, while the slowest growth was in states where the rates were already the highest. There just aren't many people left in Utah or Alaska that could get diplomas that don't have them already. The other slow-increase states are the states in the lower-right of the diagram: all four states that border Mexico, and Nevada. I imagine this is the result of Hispanic immigration, since recent Mexican-American immigrants tend to have low graduation rates.

Graduate school is the new Bachelors: 1990 to 2007

Before we start today's post, I have discovered that The Formula that Shall Not Be Named, along with not working well in general, doesn't work well in specific in openoffice, since it seems to only want to give me the ABSOLUTE VALUE. This came up when I was doing a bit of work on South Carolina, but that is going to be like Queen Beruthiel's cats for a while.

So, instead, we will look at two diagrams that both don't need any formula to be clear. The both deal with education, and the fact that (at least from my subjective viewpoint), bachelor's and graduate degrees are the new high school diploma and bachelor's degrees, respectively. (And while that sentence might be confusing, the situation is even more so.)

But, is the change across the country, or are all these overeducated people just a New England and Pacific Northwest thing?
As we can see, Bachelor's degrees seem to have increased fairly uniformly across all regions of the country, with about the same rate of increase, and with no significant outliers. This is one of the strongest correlations I have found to date.

So how about the more expensive and exclusive graduate degree? Is this, so to speak, not playing in Arkansas?


And it looks like I forgot to label my graduate school chart. Not that it matters: there are, once again, no outliers. Massachusetts is in the top right though! So it looks like the growth in graduate school is also pretty uniform, across the states.

Pomelos and a happy life: no, seriously

So as a joke between myself and Qousqous (or maybe it wasn't a joke!), I decided to plot production of Pomelos and the human development index in the world's ten leading Pomelo producing nations.

Conclusion:
Well, I bet you can figure out the conclusion yourself.
Tomorrow: maybe something relevant.

Doctors and dentists: a return to my sneaky ways

So I got side tracked about a week ago, after I did the initial doctors versus dentists post.

What I decided to look at here is which has more correlation with life expectancy: doctors per capita or dentists per capita.
Here we have doctors, and as we can see, we have a three-quarters diagram. All of the states with low life expectancies have few doctors, and there are states with high life expectancies and few doctors, and there are are states with high life expectancies and many doctors. There are not, thankfully enough, many states with many doctors and low life expectancies. But if we look at states with a life expectancy over 76, it seems that more doctors doesn't do much good. If were to be foolish enough to try to find a causation in here, we could say that at the 76 marks, adding more doctors is the point of diminishing returns.
Our dentistry and life expectancy does give us more correlation. For one thing, it shows the correlation between me being tired and being sloppy while making a diagram, which is very strong. Secondly, it shows that there is a much clearer link between dentists and life expectancy than there is for doctors. Although even the dentists are not that clearcut.

One thing to remember is that the doctors that are currently in a state and the people who are currently dying in a state are not that closely related. If someone was born in South Dakota 80 years ago and is currently dying in Florida, the doctors now in Florida don't really have much to do with however many decades of life that man was living elsewhere. Of course, this should be obvious.

I think the source of this correlation is elsewhere though, although I will leave my guesses for another day.

Montana: high school versus college. Fascinating to about three dozen people in the world, none of whom are reading this.

So mostly because I felt like doing lots of data entry after an invigorating bike ride, I decided to enter the names, high school rates and college rates of all of Montana's 56 counties into a spreadsheet, and see what I would come up with:
According to the Formula that Shall not Be Named,there is almost the exact same numerical correlation as in the diagram of Oregon counties. However, visually the diagrams are quite different. The Montana diagram looks like two separate charts. Up until about the 85% mark, there doesn't seem to be much correlation between high school and college. And then after 85, the line is pretty clear and pretty obvious.
A few things about Montana geography have to be explained here. Much of like in Oregon, the counties in the upper right have a good percentage of the population. the exceptions are Gallatin and Beaverhead, which both have colleges. This diagram gives me proof that Gallatin, which is a lot like Oregon's Benton County, actually is Montana's version of said.

So based on this, and the national and Oregon data, what do you think the correlations between the 2008 election and high school and college rates is?

In which I finally discover some real correlation

After looking at the data on high school and college a few weeks ago, and finding not much significant correlation, I decided to look at graduate school numbers. Because, as all my poor and confused and unemployed hipster friends know, graduate school is the new college.One of the things I have discovered many times since beginning this blog is that correlation is much less than what intuition would tell us. And this is a good example of that: the correlation between having a high school diploma and having an advanced degree is close to nothing. The only thing about this diagram that is expected is that many of the expected outliers show up in the expected places.

Now, lets look at the correlation between Bacherlor's Degrees and advanced degrees!

And finally, I find a strong correlation! The strongest one I have found yet in any of my scatterplots. Not only is the overall correlation clear, there are no significant outliers, at all.

Taken together, these two diagrams tell some type of story, and a curious one, at that. High school and advanced degrees are not related, while bachelor's and advanced degrees are very strongly related. What does this all mean?

Like yesterday, but with the G20

I suspected yesterday's plot was less than successful because the EU, as a group, is...quite a group. Homogeneity is a double edged sword in doing comparisons!
So I decided to do the same plot, but with the G20 countries, instead of the EU (some of which are the same countries). Besides, I only had 17 data points, since I couldn't find data for Saudi Arabia and Indonesia, and one of the 20 is the EU as a whole.
So, after that bit of introduction:

And again, we find almost nothing. There are some countries with high suicide but low homicide (South Korea, Japan), some countries with lots of both (Russia, South Africa), and one country with low suicide but high homicide (Brazil), and then everyone else. There does not seem to be any particular pattern to this data.

Maybe my next post should be something I should be SURE to find a pattern in. Hmmm...