Perhaps I misunderstand your intent, but I have the impression that you are seeking a way to deal with your data that doesn't require a lot of hard thinking about the meaning of your data and the foibles of various analytical methods. If that is indeed the case, my only comment is TINSTAAFL (= "There Is No Such Thing As A Free Lunch").
Given the information so far available, the only advice I have is to display the data as plainly as possible. This may mean in tables (not necessarily restricted to two dimensions), in scatterplots, or in other ways. It is difficult to know what useful advice to offer, not least because it is quite unclear what your variables are. You speak of "activities", and we (that is, those who have responded to your queries) have assumed that these refer to categorical variables having only two values: on/off, or present/absent, that may reasonably be coded (1/0) or (1/-1). But you also refer to "sensors", and you have not said either (1) what the sensors are sensing nor (2) what kind of information they report (again, at least one respondent has assumed that these variables also are binary (on/off), but in my lexicon a "sensor" may equally well report an intensity (volume or loudness on some scale), a position (in one, two, three, or four dimensions (one dimension of time and up to three of space), a quantity (amount of fuel remaining, operating temperature, ...), a count (number of items satisfying a particular criterion or category); inter alia). And for that matter, you have not even specified whether your "activities" are mutually exclusive, either in whole or in part: information relevant to how one might imagine making a display of elementary information. While you have referred to "regression analysis" as a style of analysis you would like (one gathers!) to invoke, you have not said which of your variables you conceive of as possible predictors, and which as possible response variables. For completeness, I include below the three postings (labelled [1], [2], and [3]) I have seen from you. If you believe you have indeed told us information that I have described above as lacking from your input, I'd be interested to know where you perceive it. ------------------------------------------------------------------------ Date: 30 May 2003 08:14:10 -0700 [1] From: Fahd <[EMAIL PROTECTED]> To: [EMAIL PROTECTED] Subject: Regression Question Does anyone know how would it be possible to convert discrete data to continous data for regression analysis? What are the implications of doing that? Also, is there any good tool for plotting regression equations (logistic curves etc.). Thanks in advance. Date: Fri, 30 May 2003 09:30:27 -0700 [2] Sorry for not being clear on that. The problem is very simple. We have a room with all sort of sensors (light, weight, software sensors...etc) and we have records of the activities people are doing along with readings from the sensors. We want to identify the correlations between the sensors and the activities, for example, if it is say a presentation then the projector sensor should show a high correlation with the activity. We are of course representing the activities with discrete values. Any thoughts about the implications of using regression. Date: Sat, 31 May 2003 09:55:36 -0700 [3] Thanks a lot for the info, but I have one other simple question. Encoding activities as 0 or 1 will allow me to identify the sensor correlations for these two activities. But what if I have a third activity, is there a way to figure out the set of sensors that correlate to the three activities instead of looking at them in pairs or is there a way to combine the correlations from the pairs? Thanks. ----------------------------------------------------------------------- Donald F. Burrill [EMAIL PROTECTED] 56 Sebbins Pond Drive, Bedford, NH 03110 (603) 626-0816 . . ================================================================= Instructions for joining and leaving this list, remarks about the problem of INAPPROPRIATE MESSAGES, and archives are available at: . http://jse.stat.ncsu.edu/ . =================================================================
