Let’s imagine a hypothetical situation. There’s an infection going round, and we want to predict the future severity of someone’s illness. There is a test that offers a good prediction. Let’s say the outcome of the test has a correlation of 0.78 with the patient's severity of infection. The problem with the test is that…
Datasets which are identical over a number of statistical properties, yet produce dissimilar graphs, are frequently used to illustrate the importance of graphical representations when exploring data. This paper presents a novel method for generating such datasets, along with several examples. Our technique varies from previous approaches in that new datasets are iteratively generated from a seed dataset through random perturbations of individual data points, and can be directed towards a desired outcome through a simulated annealing optimization strategy.
As just about every statistics student can attest, Simpson's Paradox — a statistical phenomenon where an apparent trend is reversed when you look at subgroups — is notoriously hard to explain. You can look at examples — say, the fact that US wages are rising overall, but dropping within every educational group — but that don't really help to explain the paradox. But it's not really paradox at all, but simply the fact that the disparate rate at which members of the study join the subgroups isn't accounted for in the analysis. To demonstrate this effect, the Visualizing Urban Data...
Internet and Population Statistics - market research oriented "website for international Internet usage statistics, world population data and web growth information. Here you will find statistical Internet usage data and population figures for over 265 c
hanging rootogram A diagram ( see diagram overleaf ), suggested by Tukey in 1971, for comparing an observed bar chart or histogram (with equal-width categories)
The Gapminder Graphs allow you to unveil the interactions between indicators in the OECD Factbook over time. select any two indicators for the axes in the graph, and the size of bubbles reflect the size of a third indicator of your choice. Play with time. Select countries and track and compare their performance.
with TwitterFriends you can ... * find out the hidden network of Twitter contacts that are really relevant for you. * visualize the network of your relevant contacts and their contacts * see who of your Twitter friends are online this very moment * read some stats about your Twitter account * take a look at the most conversational Twitterers or those who are posting the most links To see your relevant network and some stats about your tweeting behavior compared to other Twitter users, just enter your (or another) Twitter username: * Darren Rowse of Problogger Blog Tips wrote a nice review on TwitTip and calls TwitterFriends a "great Twitter statistics tool". Thanks, Darren! * Jason Annas even created a video explaining TwitterFriends. I think this is a great introduction to the tool, but see for yourself:
Swivel is a website where people share reports of charts and numbers. Swivel is free for public data, and charges a monthly fee to people who want to use it in private.
The purpose of Data.gov is to increase public access to high value, machine readable datasets generated by the Executive Branch of the Federal Government.
Data presentation can be beautiful, elegant and descriptive. There is a variety of conventional ways to visualize data - tables, histograms, pie charts and bar graphs are being used every day, in every project and on every possible occasion. However, to convey a message to your readers effectively, sometimes you need more than just a simple pie chart of your results. In fact, there are much better, profound, creative and absolutely fascinating ways to visualize data. Many of them might become ubiquitous in the next few years.
The burgeoning interest in R demonstrates that there’s demand for analytics to solve real, business-critical problems in a broad spectrum of companies and roles, and that some of the incumbent analytics offerings, in particular SAS and SPSS, don’t sufficiently meet the growing need for analytics in many major companies. Annotated link http://www.diigo.com/bookmark/http%3A%2F%2Fspotfire.tibco.com%2Fcommunity%2Fblogs%2Fenterpriseanalytics%2Farchive%2F2009%2F01%2F08%2Fanalytics-in-the-nyt.aspx
R. Westerholt, M. Gröbe, A. Zipf, and D. Burghardt. 10th International Conference on Geographic Information Science (GIScience 2018), volume 114 of Leibniz International Proceedings in Informatics (LIPIcs), page 63:1--63:7. Dagstuhl, Germany, Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik, (2018)