"the response variable is non-ordinal and categorical." And you propose to measure...? :) Seriously, regression assumes you have a continuous, numerical response.
You may be able to put together a model, such that the logic says that when (a) occurs, then response (y) is seen % of the time. But even that is going to involve probabiliites, numbers, and measurements. Good luck, Jay albinali wrote: > Hi, > I am using a set of data that I collected using biosensors, the goal is to > analyze the data and to infer the dependencies between the variables to > construct a bayesian network that infers the mood of a person (e.g. happy, > sad...etc). Clearly, the response variable is non-ordinal and categorical. > The model should not include interdependent sensors. I would like to get > some thoughts about using regression for such a task, is it a good choice ? > are there any other possible techniques to use? More importantly, would it > be possible to automate the building of such models? Is anyone aware of any > earlier work in that area?. Thanks. > Fahd > > . > . > ================================================================= > Instructions for joining and leaving this list, remarks about the > problem of INAPPROPRIATE MESSAGES, and archives are available at: > . http://jse.stat.ncsu.edu/ . > ================================================================= -- Jay Warner Principal Scientist Warner Consulting, Inc. 4444 North Green Bay Road Racine, WI 53404-1216 USA Ph: (262) 634-9100 FAX: (262) 681-1133 email: [EMAIL PROTECTED] web: http://www.a2q.com The A2Q Method (tm) -- What do you want to improve today? . . ================================================================= Instructions for joining and leaving this list, remarks about the problem of INAPPROPRIATE MESSAGES, and archives are available at: . http://jse.stat.ncsu.edu/ . =================================================================
