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 graphsMost 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.

From http://en.wikipedia.org/wiki/Anscombe%27s_quartet

The *x* values are the same for the first three datasets.

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 |

For all four datasets:

Property | Value |
---|---|

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.) |

SAS Visual Data Discovery combines top-selling SAS products (Base SAS, SAS/STAT® and SAS/GRAPH®), along with two interfaces (SAS® Enterprise Guide® for guided tasks and batch analysis and JMP® software for discovery and exploratory analysis).

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