an approximate answer to the right question, which is often vague, than an exact answer to the wrong question, which can always be made precise J. W. Tukey (1962, page 13), "The future of data analysis". Annals of Mathematical Statistics 33(1), pp. 1-67.
data - model - analysis - conclusions ‣ Exploratory analysis : Problem - data - analysis - model - conclusions ‣ Bayesan analysis : Problem - data - model - draft of distribution - analysis - conclusions
underlying structures Extract important variables Detect abnormalities Test suppositions issuing from the data Develop minimal models Tune to discover best parameters
do that we have to give him A not-so-bad model that ﬁts well Extreme data Robust conclusions An estimation for the parameters An error estimation for all the parameters List of the important factors and their relative individual importance Optimal parameters
comprehension, new insights and new intuitive ﬂashes of possible explanations or solutions, it will not be an orderly process. Existing means of composing and working with symbol structures penalize disorderly processes very heavily, and it is part of the real promise in the automated H-LAM/T systems of tomorrow that the human can have the freedom and power of disorderly processes ! Engelbart (1962).
example : in presenting a visual overview of the data so that categories might be hypothesised (abductively), in evaluating individual examples with respect to their “representativeness” (inductively), and showing the results of applying the new knowledge to structure the data (deductively) ! M Gahegan, M Takatsuka, M Wheeler, and F Hardisty. Introducing geovista studio : an integrated suite of visualization and computational methods for exploration and .... Computers, Environment and urban Systems, 26(4) :267–292, Jan 2002.
bihistogrammes, probability plots, lag plots, block plots, and Youden plots). ‣ Plot simple statistics (mean plots, standard deviation plots, box plots) ‣ Use multiple diagrams and put them in a page to maximise our ability to recognise patterns
results for the State of Michigan from 1998 to 2004 (see appendix A.3). A tinted lens highlights views, using labeled arrows to reveal coordination on the user’s selection of counties in the Votes v. Counties scatter plot.