On Visual Analytics Group on LinkedIn there is an interesting discussion going on:
“Based on the popularity of the Brian Bedrick’s “Looking for a Better Way to Display This Graph” post I thought it would be interesting to post another small data set and see what people come up with. I’ve found what I think might be an interesting data set here: http://www.calvin.edu/~stob/data/television.csv”
We took the data and make some graphical representations on basic quantities in our BusinessQ.
Our intent is to find some insights in the data. From four examples below it is obvious, for example, that:
1. Life Expectancy is the lowest in Ethiopia and highest in Japan; That correlates well with the fact that Ethiopia has the most people per physician ratio of 36.660
2. People per physician and People per TV does not correlate well and is not very connected
3. You can track the Female vs Male life expectancy per country in relative scale
4. You can easily spot life expectancy compared with male and female life expectancy and see distibution (Are male and female life expt. close of not so close ) per country
These are only some examples, there are many other ways to show the data. One only has to ask a question, and right tool can help him to get answers.
LinkedIn Data Visualization Challenge
Posted by Hrvoje Smolic in Business Intelligence, BusinessQ, Data Visualization, Graphs
On Visual Analytics Group on LinkedIn there is an interesting discussion going on:
“Based on the popularity of the Brian Bedrick’s “Looking for a Better Way to Display This Graph” post I thought it would be interesting to post another small data set and see what people come up with. I’ve found what I think might be an interesting data set here:
http://www.calvin.edu/~stob/data/television.csv”
We took the data and make some graphical representations on basic quantities in our BusinessQ.
Our intent is to find some insights in the data. From four examples below it is obvious, for example, that:
1. Life Expectancy is the lowest in Ethiopia and highest in Japan; That correlates well with the fact that Ethiopia has the most people per physician ratio of 36.660
2. People per physician and People per TV does not correlate well and is not very connected
3. You can track the Female vs Male life expectancy per country in relative scale
4. You can easily spot life expectancy compared with male and female life expectancy and see distibution (Are male and female life expt. close of not so close ) per country
These are only some examples, there are many other ways to show the data. One only has to ask a question, and right tool can help him to get answers.
What other interesting insights do you see?