The Value of Information Visualization - Information Visualization: Human-Centered Issues and Perspectives - page 13

I II III IV
x y x y x y x y
10.0 8.04 10.0 9.14 10.0 7.46 8.0 6.58
8.0 6.95 8.0 8.14 8.0 6.77 8.0 5.76
13.0 7.58 13.0 8.74 13.0 12.74 8.0 7.71
9.0 8.81 9.0 8.77 9.0 7.11 8.0 8.84
11.0 8.33 11.0 9.26 11.0 7.81 8.0 8.47
14.0 9.96 14.0 8.10 14.0 8.84 8.0 7.04
6.0 7.24 6.0 6.13 6.0 6.08 8.0 5.25
4.0 4.26 4.0 3.10 4.0 5.39 19.0 12.50
12.0 10.84 12.0 9.13 12.0 8.15 8.0 5.56
7.0 4.82 7.0 7.26 7.0 6.42 8.0 7.91
5.0 5.68 5.0 4.74 5.0 5.73 8.0 6.89
(a) Four datasets with different values and
the same statistical profile

(b) Dot Plot of the four datasets

Fig. 6. Anscombe's Quartet

(a) Hertzsprung Russell Diagram (b) Standard interpretation of the

Hertzsprung Russell Diagram

Fig. 7. Hertzsprung Russell Diagram and its standard interpretation

turns out that no automatic analysis method has been able to find the same
summarization, due to the noise and artifacts on the data such as the vertical
bands.
Our vision system has evolved with the human specie to help us survive
in a hostile world. We train it to avoid obstacles since we learn how to walk.
It remains remarkably effective at filtering-out noise from useful data, a very
important capability for hunters in deep forests to distinguish the prey moving