Short version: I'd go for a stacked bar graph.

Longer version: The data are complex and any graphic representation must, without lots of specialised knowledge, be a first approximation that is "scrutable and refutable" ("The creative computer", Michie & Johnson). I would therefore start by normalising all three metrics to standard deviations. I'd design my Excel spreadsheet (stop throwing things at me) so that all three were weightable, and the weight I would want to use is money, but that's probably better computed by the beancounters than the programmers. I'd start with all weightings at 1.

I would want to eliminate, before graphing, as many tunings as possible. It looks to me as though you have 10x6x6=360 different possible settings, which I would find too large for a single graph. I'd start by eliminating everything that was above average on all three metrics. Depending on how many that left, I'd proceed by eliminating those above average by x on all three or reinstating those above average on only 2, and so on until getting down to single figures. Of course, the weightings might result in the later reinstatement of originally discarded tunings. On the 42 points you have given, 18 are better (assuming lower is better) than the mean on all three metrics.

I'd be tempted to reverse the sign of the std devs so that high is good, the way the human eye (or at least mine) works naturally. Unweighted, all the selected 18 settings have P1 at 512K

Without any weightings, I've put up a quick & dirty excel file at https://gitorious.org/metrics/metrics/source/f882fd2061bb6b31ce71282ae035213cfe8bf254:. I haven't labelled the data points as I'd like because that involves VBA & doesn't play nicely with open source "Excel compatible" (hah!) spreadsheets.

Regards,

John Davies

In reply to Re: [OT] Data visualisation by davies
in thread [OT] Data visualisation by BrowserUk

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