On 9/8/2013 10:57 PM, Stephen J. Turnbull wrote:
I don't necessarily find this persuasive. It's more common when working with existing databases that you add variables than add observations.
My experience with general scientific research is the opposite. One decides on the variables to measure and then adds rows (records) of data as you measure each experimental or observational subject. New calculated variables may be added (and often are) after the data collection is complete (at least for the moment).
Time series analysis is a distinct and specialized subfield of statistics. The corresponding data collections is often different: one may start with a fixed set of subjects (50 US states for instance) and add 'variables' (population in year X) indefinitely. Much economic statistics is in this category.
A third category is interaction analysis, where the data form a true matrix where both rows and columns represent subjects and entries represent interaction (how many times John emailed Joe, for instance).
-- Terry Jan Reedy _______________________________________________ Python-Dev mailing list Python-Dev@python.org https://mail.python.org/mailman/listinfo/python-dev Unsubscribe: https://mail.python.org/mailman/options/python-dev/archive%40mail-archive.com
