Tag Archives: data

Wind Power Provided Over 1% of Global Electricity in 2007

graph of global installed wind power capacity

Data from World Wind Energy Association, for installed Mega Watts of global wind power capacity in 2007. 19,696 MW of capacity were added in 2007, bringing the total to 93,849 MW. Europe accounts for 61% of installed capacity, Germany accounts for 24% and the USA 18%.

The graph shows the top 10 producers (with the exceptions of Denmark and Portugal) and includes Japan (which is 13th).

Best Research University Rankings – 2008

The annual ranking of research Universities are available from Shanghai’s Jiao Tong University. The methodology values publications and faculty awards which provides a better ranking of research (rather than teaching). Results from the 2008 rankings of Top 500 Universities worldwide, country representation of the top schools:

location	Top 100	% of World Population	% of World GDP	% of top 500
USA	54	4.6%	27.2%	31.6%
United Kingdom	11	0.9	4.9	8.3
Germany	6	1.3	6.0	8.0
Japan	4	2.0	9.0	6.2
Canada	4	0.5	2.6	4.2
Sweden	4	0.1	0.8	2.2
France	3	0.8	4.6	4.6
Switzerland	3	0.1	0.8	1.6
Australia	3	0.3	1.6	3.0
Netherlands	2	0.2	1.4	2.4
Denmark	2	0.1	0.6	0.8
Finland	1	0.1	0.4	1.2
Norway	1	0.1	0.7	0.8
Israel	1	0.1	0.3	1.2
Russia	1	2.2	2.0	0.4
China		20.5	6.6	6.0
India		17.0	1.9	0.4

There is little change in most of the data from last year, which I think is a good sign, it wouldn’t make much sense to have radical shifts over a year in these rankings. Japan lost 2 schools in the top 100, France lost 1. Denmark (Aarhus University) and Australia (University of Sydney) gained 1. Last year there was a tie so there were 101 schools in the top 100.

The most dramatic data I noticed is China’s number of top 500 schools went from 14 to 30, which made me a bit skeptical of what caused that quick change. Looking more closely last year they reported the China top 500 totals as (China 14, China-Taiwan 6 and China-Hong Kong 5). That still gives them an impressive gain of 5 schools.

Singapore has 1 in the 102-151 range. Taiwan has 1 ranked in the 152-200 range, as do Mexico, Korea and Brazil. China has 9 in the 201-302 range (including 3 in Hong Kong). India has 2 in the 303-401 range.

University of Wisconsin – Madison is 17th again 🙂 My father taught there while I grew up.
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Study Finds No Measurable Benefit to Flu Shots

Do Flu Shots For The Elderly Save Lives? Just Washing Hands Works Better, Says Study

The widely-held perception that the influenza vaccination reduces overall mortality risk in the elderly does not withstand careful scrutiny, according to researchers in Alberta. The vaccine does confer protection against specific strains of influenza, but its overall benefit appears to have been exaggerated by a number of observational studies that found a very large reduction in all-cause mortality among elderly patients who had been vaccinated.

The study included more than 700 matched elderly subjects, half of whom had taken the vaccine and half of whom had not. After controlling for a wealth of variables that were largely not considered or simply not available in previous studies that reported the mortality benefit, the researchers concluded that any such benefit “if present at all, was very small and statistically non-significant and may simply be a healthy-user artifact that they were unable to identify.”
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“Over the last two decades in the United Sates, even while vaccination rates among the elderly have increased from 15 to 65 percent, there has been no commensurate decrease in hospital admissions or all-cause mortality

University Web Presence Rankings

The Webometrics Ranking of University Web Sites provides some interesting data. I don’t remember reading this last year, but they state on the site now: “The original aim of the Ranking was to promote Web publication, not to rank institutions. Supporting Open Access initiatives, electronic access to scientific publications and to other academic material are our primary targets.” I support those goals, I am not totally convinced this is the most effective measure to do that but it provides one way of ranking web presence of universities. I am not that convinced this does a good job of ranking the web presences of universities but I think it is of some interest so I decided to post on the results.

graph of universities web presence

Country	% top 200	% top 500	% World Population	Jiao Tong top 101
USA	53	37.8	4.6	54
Germany	7.5	9.4	1.3	6
United Kingdom	5.5	7.2	0.9	11
Canada	8.5	5	0.5	4
Australia	3	2.8	0.3	2
Italy	0.5	2.8	0.9	1
Japan	1.5	2.4	2	6
France	0.5	2.4	0.9	4
Netherlands	4	2.2	0.3	2
Sweden	3	2	0.1	4
Switzerland	2	1.6	0.1	3
Taiwan	0.5	1.6	0.4	0
Finland	0.5	1.4	0.1	1
China	0.5	1.2	20.1	0
Portugal	0	1.2	0.2	0

500 Year Floods

Why you can get ‘500 year floods’ two years in a row by Anne Jefferson:

Flood probabilities are based on historical records of stream discharge. Let’s use the Iowa River at Marengo, Iowa as an example. It reached a record discharge of 46,600 cubic feet per second* (1320 m3/s) on 12 June. That flow was estimated to have a 500 year recurrence interval, based on 51 years of peak flow records
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When you are extrapolating beyond your data by an order of magnitude, the highest points in the dataset start to have a lot of leverage. Let’s imagine that there’s another big flood on the Iowa River next year and we do the same analysis. Now our dataset has 52 points, with the highest being the flood of 2008. When that point is included in the analysis, a discharge of 46,600 cubic feet per second* (1320 m3/s) has a recurrence interval of <150 years (>0.6%). It’s still a darn big flow, but it doesn’t sound quite so biblical anymore.
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Urbanization and the adding of impervious surface is one cause of increasing flood peaks, but in Iowa, a more likely culprit is agricultural.

This post is a good explanation that the 500 year flood idea is just way of saying .2% probability (that some people confuse as meaning it can only happen every 500 years). But I actually am more interested in the other factor which is how much estimation is in “500 year prediction.” We don’t have 500 years of data. And the conditions today (I believe) are much more likely to create extreme conditions. So taking comfort in 500 year (.2%), or even 100 year (1% probability) flood “predictions” is dangerous.

It would seem to me, in fact, actually having a 500 year flood actually increases the odds for it happening again (because the data now includes that case which had not been included before). It doesn’t actually increase the likelihood of it happening but the predictions we make are based on the data we have (so given that it happens our previous 500 year prediction is questionable). With a coin toss we know the odds are 50%, getting 3 heads in a row doesn’t convince us that our prediction was bad. And therefore the previous record of heads or tails in the coin toss have no predictive value.

I can’t see why we would think that for floods. With the new data showing a flood, (it seems to me) most any model is likely to show an increased risk (and pretty substantial I would think) of it happening again in the next 100 years (especially in any area with substantial human construction – where conditions could well be very different than it was for our data that is 20, 40… years old). And if we are entering a period of more extreme weather then that will likely be a factor too…

The comments on the original blog post make some interesting points too – don’t miss those.

Women Choosing Other Fields Over Engineering and Math

graph of science and engineering degrees by gender in the USA 1966-2005

The graph shows college degrees granted in the USA. This topic sets up one for criticism, but I believe it is more important to examine the data and explore the possible ideas than to avoid anything that might be questioned by the politically correct police. An import factor, to me anyway, is that women are now graduating from college in far higher numbers than men. And in many science fields female baccalaureate graduates outnumber male graduates (psychology [67,000 to 19,000], biology[42,000 to 26,000], anthropology, sociology [20,000 to 8,000]) while men outnumber women in others (math [7,000 to 6,000], engineering [53,000 to 13,000], computer science [39,000 to 11,000], physics [3,000 to 900]).

Data on degrees awarded men and women in the USA in 2005, from NSF*:

Field	Bachelors		Master’s		Doctorate
	Women	Men	Women	Men	Women	Men
Biology	42,283	25,699	4,870	3,229	3,105	3,257
Computer Science	11,235	39,329	5,078	12,742	225	909
Economics	8,141	17,023	1,391	2,113	355	827
Engineering	13,197	52,936	7,607	26,492	1,174	5,215
Geosciences	1,660	2,299	712	973	243	470
Physics	903	3,307	427	1,419	200	1,132
Psychology	66,833	19,103	12,632	3,444	2,264	211
Sociology	20,138	8,438	920	485	343	211
All S&E	235,197	230,806	53,051	66,974	10,533	17,405

What does this all mean? It is debatable, but I think it is very good news for the efforts many have made over the last few decades to open up opportunities for women. I still support efforts to provide opportunities for girls to get started in science and engineering but I think we have reached the day when the biggest concern is giving all kids better math and science primary education (and related extracurricular activities). Also continued focus and effort on the doctorate and professional opportunities for women is warranted.
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Surprising New Diabetes Data

But these measures are only surrogates for disease. And in many cases, the connection between “better” numbers and better health is tenuous. In the case of cholesterol, many people won’t see a health benefit from lower numbers.

Now comes yet another sobering reminder that lowering a surrogate marker doesn’t necessarily bring better health. On Feb. 6, the National Institutes of Health announced it was halting a key trial for diabetes. Researchers had hoped the trial, dubbed ACCORD (Action to Control Cardiovascular Risk in Diabetes), would show that more aggressive lowering of blood sugar would significantly reduce deaths. Instead, the opposite happened. More people in the intensive treatment group died than in the group getting standard care. “A thorough review of the data shows that the medical treatment strategy of intensively reducing blood sugar below current clinical guidelines causes harm in these…patients,” says Dr. Elizabeth Nabel, director of the National Heart, Lung & Blood Institute.

Scientific study often results in less than clear conclusions, especially in complex systems. There is great difficulty understanding what is actually going on, what interactions are present, what factors are significant, etc.. One of the great problems with the low level of scientific literacy in the USA is so many people think science is about simple absolute truth.

Scientific inquiry, especially related to health care, must attempt to gain insights from confusing signals. To gain scientific literacy one must understand basics concepts, like data is a proxy for what you aim to understand. To understand yourself you need to accept that science is not math. For a long time we are going to have to do our best to build up our understanding of human health (and other complex systems) as best we can. We need to be able to sort out what are solid conclusion, what are guesses, what seem like reasonable explanation and what level of confidence we can have in statements.

It is not enough to learn facts we need to be able to think scientifically and comprehend the subtleties surrounding the advances in scientific understanding. Some criticize newspapers and popular science for providing too simplistic a view of new scientific knowledge. While this can be a problem I really see the problem much more serious if people read obviously overly simplistic articles and don’t understand that it is just scratching the surface. The reader needs to take responsibility too. I enjoy many great articles that gloss over many of the details but provide a quick view of intriguing new breakthroughs.

Bigger Impact: 15 to 18 mpg or 50 to 100 mpg?

This is a pretty counter-intuitive statement, I believe:

You save more fuel switching from a 15 to 18 mpg car than switching from a 50 to 100 mpg car.

But some simple math shows it is true. If you drive 10,000 miles you would use: 667 gallons, 556 gallons, 200 gallons and 100 gallons. Amazing. I must admit, when I first read the quote I thought that it must be an wrong. But there is the math. You save 111 gallons improving from 15 mpg to 18 mpg and just 100 improving from 50 to 100 mpg. Other than those of you who automatically guess that whatever seems wrong must be the answer when you see a title like this I can’t believe anyone thinks 15 to 18 mpg is the change that has the bigger impact. It is great how a little understanding of math can help you see the errors in your initial beliefs. Via: 18 Is Enough.

It also illustrates that the way the data is presented makes a difference. You can also view 100 mpg as 1/100 gallon per mile, 2/100 gallons per mile, 5.6/100 gpm and 6.7 gpm. That way most everyone sees that the 6.7 to 5.6 gpm saves more fuel than 2 to 1 gpm does. Mathematics and scientific thinking are great – if you are willing to think you can learn to better understand the world we live in every day.

Playing Dice and Children’s Numeracy

My father, Willaim Hunter, a professor of statistics and of Chemical Engineering at the University of Wisconsin, was a guest speaker for my second grade class (I think it was 2nd) to teach us about numbers – using dice. He gave every kid a die. I remember he asked all the kids what number do you think will show up when you roll the die. 6 was the answer from about 80% of them (which I knew was wrong – so I was feeling very smart).

Then he had the kids roll the die and he stood up at the front to create a frequency distribution of what was actually rolled. He was all ready for them to see how wrong they were and learn it was just as likely for any of the numbers on the die to be rolled. But as he asked each kid about what they rolled something like 5 out of the first 6 said they rolled a 6. He then modified the exercise a bit and had the kid come up to the front and roll the die on the teachers desk. Then my Dad read the number off the die and wrote on the chart 🙂

This nice blog post, reminded me of that story: Kids’ misconceptions about numbers — and how they fix them

in the real study, conducted by John Opfer and Rober Siegler, the kids used lines with just 0 and 1000 labeled. They were then given numbers within that range and asked to draw a vertical line through the number line where each number fell (they used a new, blank number line each time). The figure above represents (in red) the average results for a few of the numbers used in the study. As you can see, the second graders are way off, especially for lower numbers. They typically placed the number 150 almost halfway across the number line! Fourth graders perform nearly as well as adults on the task, putting all the numbers in just about the right spot.

But there’s a pattern to the second-graders’ responses. Nearly all the kids (93 were tested) understood that 750 was a larger number than 366; they just squeezed too many large numbers on the far-right side of the number line. In fact, their results show more of a logarithmic pattern than the proper linear pattern.

Correlation is Not Causation

Why so much medical research is rot:

People born under the astrological sign of Leo are 15% more likely to be admitted to hospital with gastric bleeding than those born under the other 11 signs. Sagittarians are 38% more likely than others to land up there because of a broken arm. Those are the conclusions that many medical researchers would be forced to make from a set of data presented to the American Association for the Advancement of Science by Peter Austin of the Institute for Clinical Evaluative Sciences in Toronto. At least, they would be forced to draw them if they applied the lax statistical methods of their own work to the records of hospital admissions in Ontario, Canada, used by Dr Austin.

Dr Austin, of course, does not draw those conclusions. His point was to shock medical researchers into using better statistics, because the ones they routinely employ today run the risk of identifying relationships when, in fact, there are none. He also wanted to explain why so many health claims that look important when they are first made are not substantiated in later studies.

As I said in, Seeing Patterns Where None Exists: “Page 8 of Statistics for Experimenters by George Box, William Hunter (my father) and Stu Hunter (no relation) shows a graph of the population (of people) versus the number of storks which shows a high correlation. “Although in this example few would be led to hypothesize that the increase in the number of storks caused the observed increase in population, investigators are sometimes guilty of this kind of mistake in other contexts.'”