I gave a presentation on this topic a few years ago at the Joint Statistical Meetings, and I disagree that this is a simple operation. The two major difficulties are (1) weighted histograms are not simple to define and understand, and (2) a weighted fit is not well-defined. The WEIGHT statement in UNIVARIATE has the following meaning (from the doc): The UNIVARIATE procedure uses the values of the WEIGHT variable to modify the computation of a number of summary statistics by assuming that the variance of the th value of the analysis variable is equal to , where is an unknown parameter. The implications of this is that each observation is from a different distribution! You can't put a single "fitted curve" on top of a histogram because no such curve exists. Even if you solve for the common variance, , that doesn't do you much good because you can't use it to overlay a density estimate or to do a GOF test. The problem of constructing a weighted statistical graphic is still an area of research. I'll give you the same challenge I gave the statisticians at JSM: find a paper (in a reputable journal) in which weighted histograms are defined and weighted fits are described. Send that paper to me at SAS and I'll pass it on to the UNIVARIATE developer. As to where should you go from here, if the weights are inverse probablities, then perhaps you can define FREQ = 1/WEIGHT and use a FREQ statement. Histograms, fits, GOF tests, etc, are well-defined for count data. It sounds like this might be survey data, and if so you should use the SURVEY* procs (SURVEYMEANS, SURVEYFREQ, etc) to analyze survey data. These procs make the correct adjustments when computing variance of variables.
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