On 27 Mar 2003 05:02:32 -0800, [EMAIL PROTECTED] (Patricia Bacon) wrote: > Dear readers of EdStat, > > I have a data set of the number of tourists who came > to Brazil > from 1990 to 2000. I have 10 different nationalities > of tourists > and I also have the type of accommodation they were > in. I also > have the different cities in Brazil and the number of > tourists > for each one, also by years and even months, by kind > of > accomodation and by nationality.
It is going to be easier if you can collapse across categories. Are all the years similar? Collapse into 5-years versus 6 years? Are some of the nationalities similar? Combine? Are some of the cities similar? - I am referring to the their cross tabulations with the other variables. Do you know of trends that anyone has touted? (Tourist industry?) - In some fashion, do take into account what it is that people know or expect. > > I would like to extract the information of those data > in order > to find if there are patterns among the tourists, the > evolution > of them, the differences in the kind of accomodation, > if it has changed through the years and if there are > seasonal patterns. > > I checked cluster analysis, but since I have ten > variables corresponding to the years, I don't know if > this is correct. - tough to apply, tough to make sense of. > I was told that a correspondence analysis should be > appropriate (for the tourists and the kind of accomodation), - might do some useful exploring of the sort I suggested. > but since I have the variable year and month I don't > know how to deal > with it and maybe there are more appropriate > techniques. > In order to show the evolution, I think I should > extract the > information for each nationality as a time series, but > I don't know > if I could also show the evolution for several > nationalities altogether. > As I have the data by months, ARIMA models should fit > (I guess). Segment by season? > > If someone knows how I can deal with this data, the > Kind of > techniques I should use and if there are any > references of related > things, I would appreciate it so much. I should suggest some quick and easy tabulations at the start, so you can learn more about the scope of the problem -- Where the interesting variations are *apt* to occur. (Would a time-series program tell you something, from the start? - Maybe, but it would not be as easy to start with, as tabulations.) -- Rich Ulrich, [EMAIL PROTECTED] http://www.pitt.edu/~wpilib/index.html . . ================================================================= Instructions for joining and leaving this list, remarks about the problem of INAPPROPRIATE MESSAGES, and archives are available at: . http://jse.stat.ncsu.edu/ . =================================================================
