One of the seminal papers establishing the importance of data visualization (as it is now called) was the 1973 paper by F J Anscombe in http://www.sjsu.edu/faculty/gerstman/StatPrimer/anscombe1973.pdf
It has probably the most elegant introduction to an advanced statistical analysis paper that I have ever seen-
1. Usefulness of graphs
Most textbooks on statistical methods, and most statistical computer programs, pay too little attention to graphs. Few of us escape being indoctrinated with these notions:
(1) numerical calculations are exact, but graphs are rough;
(2) for any particular kind of statistical data there is just one set of calculations constituting a correct statistical analysis;
(3) performing intricate calculations is virtuous, whereas actually looking at the data is cheating.
A computer should make both calculations and graphs. Both sorts of output should be studied; each will contribute to understanding.
Of course the dataset makes it very very interesting for people who dont like graphical analysis too much.
The x values are the same for the first three datasets.
For all four datasets:
|Mean of x in each case||9 exact|
|Variance of x in each case||11 exact|
|Mean of y in each case||7.50 (to 2 decimal places)|
|Variance of y in each case||4.122 or 4.127 (to 3 d.p.)|
|Correlation between x and y in each case||0.816 (to 3 d.p.)|
|Linear regression line in each case||y = 3.00 + 0.500x (to 2 d.p. and 3 d.p. resp.)|