Tag Archives: data

Ritalin Doesn’t Show Long Term Effectiveness for ADHD

From the New York Times opinion piece, Ritalin Gone Wrong, by L. Alan Sroufe is a professor emeritus of psychology at the University of Minnesota’s Institute of Child Development:

Attention-deficit drugs increase concentration in the short term, which is why they work so well for college students cramming for exams. But when given to children over long periods of time, they neither improve school achievement nor reduce behavior problems. The drugs can also have serious side effects, including stunting growth.
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To date, no study has found any long-term benefit of attention-deficit medication on academic performance, peer relationships or behavior problems, the very things we would most want to improve. Until recently, most studies of these drugs had not been properly randomized, and some of them had other methodological flaws.

But in 2009, findings were published from a well-controlled study that had been going on for more than a decade, and the results were very clear… At first this study suggested that medication, or medication plus therapy, produced the best results. However, after three years, these effects had faded, and by eight years there was no evidence that medication produced any academic or behavioral benefits.

As I have written before I am skeptical of the amount of drug use our health care system encourages: Lifestyle Drugs and Risk.

H-index Rank for Countries: for Science Publications

The SCImago Journal and Country Rank provides journal and country scientific indicators. As stated in previous posts, these types of rankings have limitations but they are also interesting. The table shows the top 6 countries by h-index and then some others I chose to list (the top 6 repeat from my post in 2008 – Country H-index Rank for Science Publications). The h-index provides a numeric indication of scientific production and significance (by looking at the citations given papers by other papers). Read more about the h-index (Hirsh index).

Country	h-index	h-index (2007)	% of World Population	total Cites
USA	1,139	793	4.5%	87,296,701
United Kingdom	689	465	.9%	21,030,171
Germany	607	408	1.2%	17,576,464
France	554	376	1.0%	12,168,898
Canada	536	370	.5%	10,375,245
Japan	527	372	1.8%	14,341,252
Additional countries of interest
18) China	279	161	19.4%	5,614,294
21) South Korea	258	161	.7%	2,710,566
22) Brazil	239	148	2.8%	1,970,704
25) India	227	146	17.5%	2,590,791
31) Singapore	196		.01%	871,512

Engineering Again Dominates The Highest Paying College Degree Programs

As usual most of the highest paying undergraduate college degrees in the USA are engineering. Based on data from payscale, all of the top 10 highest paying fields are in engineering. The highest non-engineering fields are applied mathematics and computer science. Petroleum Engineering salaries have exploded over the last few years to $93,000 for a starting median salary, more than $30,000 above the next highest paying degree.

Mid-career median salaries follow the same tendency for engineering degrees, though in this case, 3 of the top 10 salaries (15 years into a career) are for those with non-engineering degrees: applied mathematics, physics and economics.

	Highest Paid Undergrad College Degrees
Degree	Starting Median Salary	Mid-Career Median Salary	2009 starting salary
Petroleum Engineering	$93,000	$157,000
Chemical Engineering	$64,800	$108,000	$65,700
Nuclear Engineering	$63,900	$104,000
Computer Engineering	$61,200	$99,500	$61,700
Electrical Engineering	$60,800	$104,000	$60,200
Aerospace Engineering	$59,400	$108,000	$59,600
Material Science and Engineering	$59,400	$93,600
Industrial Engineering	$58,200	$97,400	$57,100
Mechanical Engineering	$58,300	$97,400	$58,900
Software Engineering	$56,700	$91,300
Applied Mathematics	$56,400	$101,000
Computer Science	$56,200	$97,700	$56,400

More degrees are shown in the following table, but this table doesn’t include all the degree; it just shows a sample of the rest of the degrees.
Continue reading →

Wind Power Capacity Up 170% Worldwide from 2005-2009

graph of global installed wind power capacity from 2005-2009

Chart showing global installed wind energy capacity by Curious Cat Science and Engineering Blog, Creative Commons Attribution. Data from World Wind Energy Association, for installed Megawatts of global wind power capacity.

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Globally 38,025 MW of capacity were added in 2009, bringing the total to 159,213 MW, a 31% increase. The graph shows the top 10 producers (with the exceptions of Denmark and Portugal) and includes Japan (which is 13th).

Wind power is now generating 2% of global electricity demand, according to the World Wind Energy Association. The countries with the highest shares of wind energy generated electricity: Denmark 20%, Portugal 15%, Spain 14%, Germany 9%. Wind power employed 550,000 people in 2009 and is expected to employ 1,000,000 by 2012.

From 2005 to 2009 the global installed wind power capacity increased 170% from 59,033 megawatts to 159,213 megawatts. The percent of global capacity of the 9 countries in the graph has stayed remarkably consistent: from 81% in 2005 growing slowly to 83% in 2009.

Over the 4 year period the capacity in the USA increased 284% and in China increased 1,954%. China grew 113% in 2009, the 4th year in a row it more than doubled capacity. In 2007, Europe had for 61% of installed capacity and the USA 18%. At the end of 2009 Europe had 48% of installed capacity, Asia 25% and North America 24%.

Hans Rosling on Global Population Growth

Hans Rosling provides another interesting TED talk. As he mentions economics plays a huge role in whether we will slow population growth. The economic condition plays a huge role in child survival which he calls “the new green.” Meaning the fate of the environment is tied to increasing child survival (and decreasing poverty). There are many important factors that will impact the fate of the environment but a big factor is world population.

Google Prediction API

This looks very cool.

The Prediction API enables access to Google’s machine learning algorithms to analyze your historic data and predict likely future outcomes. Upload your data to Google Storage for Developers, then use the Prediction API to make real-time decisions in your applications. The Prediction API implements supervised learning algorithms as a RESTful web service to let you leverage patterns in your data, providing more relevant information to your users. Run your predictions on Google’s infrastructure and scale effortlessly as your data grows in size and complexity.

Accessible from many platforms: Google App Engine, Apps Script (Google Spreadsheets), web & desktop apps, and command line.

The Prediction API supports CSV formatted training data, up to 100M in size. Numeric or unstructured text can be sent as input features, and discrete categories (up to a few hundred different ones) can be provided as output labels.

Uses:
Language identification
Customer sentiment analysis
Product recommendations & upsell opportunities
Diagnostics
Document and email classification
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Statistics Insights for Scientists and Engineers

My father was a engineer and statistician. Along with George Box and Stu Hunter (no relation) they wrote Statistics for Experimenters (one of the potential titles had been Statistics for Engineers). They had an interest in bringing applied statistics to the work of scientists and engineers and I have that interest also. To me the key trait for applied statistics is to help experimenters learn quickly: it is an aid in the discovery process. It should not be a passive tool for analysis (which is how people often think of statistics).

JosÃ© RamÃrez studied applied and industrial statistics at the University of Wisconsin – Madison with my father and George Box. And now has a book and blog on taking statistics to engineers and scientists

The book is primarily written for engineers and scientists who need to use statistics and JMP to make sense of data and make sound decisions based on their analyses. This includes, for example, people working in semiconductor, automotive, chemical and aerospace industries. Other professionals in these industries who will find it valuable include quality engineers, reliability engineers, Six Sigma Black Belts and statisticians.
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For those who want a reference for how to solve common problems using statistics and JMP, we walk through different case studies using a seven-step problem-solving framework, with heavy emphasis on the problem setup, interpretation, and translation of the results in the context of the problem.

For those who want to learn more about the statistical techniques and concepts, we provide a practical overview of the underpinnings and provide appropriate references. Finally, for those who want to learn how to benefit from the power of JMP, we have loaded the book with many step-by-step instructions and tips and tricks.

Stat Insights is a blog by JosÃ© and Brenda RamÃrez.

Analyzing and Interpreting Continuous Data Using JMP by JosÃ© and Brenda RamÃrez. view chapter 1 online.

[We] have focused on making statistics both accessible and effective in helping to solve common problems found in an industrial setting. Statistical techniques are introduced not as a collection of formulas to be followed, but as a catalyst to enhance and speed up the engineering and scientific problem-solving process. Each chapter uses a 7-step problem-solving framework to make sure that the right problem is being solved with an appropriate selection of tools.

Florence Nightingale: The passionate statistician

She brought about fundamental change in the British military medical system, preventing any such future calamities. To do it, she pioneered a brand-new method for bringing about social change: applied statistics.
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he statistics changed Nightingale’s understanding of the problems in Turkey. Lack of sanitation, she realized, had been the principal reason for most of the deaths, not inadequate food and supplies as she had previously thought.
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As impressive as her statistics were, Nightingale worried that Queen Victoria’s eyes would glaze over as she scanned the tables. So Nightingale devised clever ways of presenting the information in charts. Statistics had been presented using graphics only a few times previously, and perhaps never to persuade people of the need for social change.

Applied statistics is a tool available to all to achieve great improvement. Unfortunately it is still very underused. As George Box says: applied statistics is not about proving a theorem, it’s about being curious about things. The goal of design of experiments is to learn and refine your experiment based on the knowledge you gain and experiment again. It is a process of discovery.

Learning Design of Experiments with Paper Helicopters

Photo showing the helicopter test track by Brad

Dr. George E.P. Box wrote a great paper on Teaching Engineers Experimental Design With a Paper Helicopter that can be used to learn principles of experimental design, including – conditions for validity of experimentation, randomization, blocking, the use of factorial and fractional factorial designs and the management of experimentation.

I ran across an interesting blog post on a class learning these principles today – Brad’s Hella-Copter:

For our statistics class, we have been working hard on a Design of Experiments project that optimizes a paper helicopter with respect to hang time an accuracy of a decent down a stairwell.
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We were to design a helicopter that would drop 3 stories down within the 2ft gap between flights of stairs.
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[design of experiments is] very powerful when you have lots of variables (ie. paper type, helicopter blade length, blade width, body height, body width, paperclip weights, etc) and not a lot of time to vary each one individually. If we were to individually change each variable one at a time, we would have made over 256 different helicopters. Instead we built 16, tested them, and got a feel for which variables were most important. We then focused on these important variables for design improvement through further testing and optimization.

The Value of Displaying Data Well

Anscombe’s quartet: all four sets are identical when examined statistically, but vary considerably when graphed. Image via Wikipedia.

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Anscombe’s quartet comprises four datasets that have identical simple statistical properties, yet are revealed to be very different when inspected graphically. Each dataset consists of eleven (x,y) points. They were constructed in 1973 by the statistician F.J. Anscombe to demonstrate the importance of graphing data before analyzing it, and of the effect of outliers on the statistical properties of a dataset.

Of course we also have to be careful of drawing incorrect conclusions from visual displays.

For all four datasets:

Property	Value
Mean of each x variable	9.0
Variance of each x variable	10.0
Mean of each y variable	7.5
Variance of each y variable	3.75
Correlation between each x and y variable	0.816
Linear regression line	$y = 3 + 0.5 x$

Edward Tufte uses the quartet to emphasize the importance of looking at one’s data before analyzing it in the first page of the first chapter of his book, The Visual Display of Quantitative Information.