MOST POPULAR INFOGRAPHICS. Any set of figures needs adjusting before it can be usefully reported. Fox News was excited: "Unplanned children develop more slowly, study finds.

" The Telegraph was equally shrill in its headline ("IVF children have bigger vocabulary than unplanned children"). And the British Medical Journal press release drove it all: "Children born after an unwanted pregnancy are slower to develop. " The last two, at least, made a good effort to explain that this effect disappeared when the researchers accounted for social and demographic factors.

But was there ever any point in reporting the raw finding, from before this correction was made? I will now demonstrate, with a nerdy table illustration, how you correct for things such as social and demographic factors. Correcting for an extra factor is best understood by doing something called "stratification". But then some clever person comes along and says: wait, maybe this whole finding is confounded by the fact that drinkers also smoke cigarettes? Data journalism and data visualization from the Datablog.

My top ten data.gov.uk datasets - a guest post by Simon Rogers. Data.gov.uk has become one of the finest national open data initiatives in the world - it now has more data than the mighty data.gov in the US, with 4,223 datasets, compared to 2,876 over the Atlantic.

It's not perfect - far too many links take you to front pages on other sites, rather than the data itself. It could also do with more help for the less-experienced user, witness the multitude of downloads on the Treasury's Combined Online Information System (COINS) dataset ( But nevertheless, what a resource. And where it really comes into its own is in the publication of immense datasets previously kept within the confines of the civil service, many of which show highly local data. So, if I had to pick my top ten data.gov.uk datasets here is where I would start: 1) National Public Transport Data Repository (NPTDR) If you want a complete dataset, look no further. 2) Combined Online Information System 3) Youth cohort study.

Marathon 2010. A 24-hour student data visualization competition Click here to download Visualizing Marathon 2010 Poster.

Welcome Visualizing.org is proud to have held the inaugural Visualizing Marathon: a 24-hour student data visualization competition. Inspired by robotics competitions and science fairs, the Marathon was created to give design students an opportunity to collaborate and use design to help tackle real-world issues. Journalism in the Age of Data: A Video Report on Data Visualization by Geoff McGhee.

Let's Intersect! Conditional Risk. Doctor Who: Every single journey through time detailed detailed by Information is Beautiful. As a spreadsheet. Doctor Who time travels of the Doctor: Information is Beautiful gets the data - what can you do?

Illustration: David McCandless for the Guardian Last year, I created a visualisation of Time travel in TV & Films. You know. Star Trek, Back To The Future, Planet Of The Apes etc. Escher-like internet map could speed online traffic - tech - 08 September 2010. A novel map of the internet created by Marián Boguñá and colleagues at the University of Barcelona, Spain, could help make network glitches a thing of the past.

Statistical Analysis - Stack Exchange. Research tips. When watching the TV news, or reading newspaper commentary, I am frequently amazed at the attempts people make to interpret random noise.

For example, the latest tiny fluctuation in the share price of a major company is attributed to the CEO being ill. When the exchange rate goes up, the TV finance commentator confidently announces that it is a reaction to Chinese building contracts. No one ever says “The unemployment rate has dropped by 0.1% for no apparent reason.” What is going on here is that the commentators are assuming we live in a noise-free world.

Statistics How To.

Statistics blogs. Statistical modeling, causal inference, and social science: Blog of Andrew Gelman's research group, featuring Bayesian statistics, multilevel modeling, causal inference, political science, decision theory, public health, sociology, economics, and literatu. How to visualize data with cartoonish faces ala Chernoff. FlowingData reader Chris asks: I was wondering, have you ever considered doing a Chernoff faces tutorial for R?

I think Chernoff faces are pretty interesting and I haven't seen much about them on the web. This wasn't the first time someone's asked how to make Chernoff faces, so I did a quick search. Guess what. There's an R package for that. Why visualise data? Why visualise data? In the introduction to his classic text, The Visual Display of Quantitative Information, Edward Tufte answers this question in three words. “Graphics reveal data”. Problem solving flowchart (slightly crass) Stochastic. Stochastic comes from the Greek word στόχος (stokhos, "aim").

Mathematical theory[edit] The use of the term stochastic to mean based on the theory of probability goes back to a 1917 publication by Ladislaus Bortkiewicz (1868–1931).[3] Bortkiewicz used it in the sense of making conjectures that the Greek term has borne since the days of the ancient philosophers, and after the title of Ars Conjectandi that Jakob Bernoulli gave to his work (published in 1713) on probability theory.[4] In mathematics, specifically in probability theory, the field of stochastic processes has been[when?] A major area of research. [citation needed] Artificial intelligence[edit] In artificial intelligence, stochastic programs work by using probabilistic methods to solve problems, as in simulated annealing, stochastic neural networks, stochastic optimization, genetic algorithms, and genetic programming.

Natural science[edit] Randomness. There was a query on the SAS mailing list today - someone got inconsistent results for confidence intervals between Excel and SAS.

In Excel, they were using the confidence() function, which I'd not come across before. And I'm glad about that. See, to calculate a confidence interval, you multiply the standard error of the distribution for the critical value from the t-distribution. You can find that value using (say) R, with the qt() function or Excel, with the tinv() function. The t-distribution approximates the normal distribution as the sample size increases - you need a sample size of infinity for them to be exactly the same, but if the same size is large enough, then it's close. Skyrails Blog. Someone finally (thanks Christian) sent a mail in the skyrails-public mailing list, asking some questions, so I answered in the mailing list.

It'll just become an FAQ here instead. > I am aware of that skyrails is a sprout of a PhD research > project and these details may change. Actually, I'm not doing a PhD *yet*.