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Created:
Oct 2, 2009 10:49 AM
| Last Modified:
Oct 2, 2009 7:49 AM
(6772 views)
| Posted in reply to message from ms 09-25-2009
Yes, it is also called bandwidth.
If data is just simply asymmetric, then many of the available distributions will fit it. Particularly the Johnson distributions and Glog. They can handle heavy skewness. If it's not too skewed, then perhaps one of the common ones will work too, like Weibull or LogNornal. For different shapes of skewness, the fitted parameters will be different of course.
If data is multimodal, then the only option now is the smooth curve fit. If the shape of each data set is relatively the same, then the estimated kernel std will be roughly similar. If the shapes are very different, then the kernel std is different. Don't try to compare the kernel std's, but it is ok to make a rough comparison of the quantiles. I haven't formally investigated the performance of the smooth curve fit across different samples that are supposed to be similar or different, so I am guessing at these things. And, of course, quantiles can vary depending on overfitting or underfitting of the smooth curve.
If you want to estimate quantiles without worrying about a fitted distribution, there is an empirical quantile function in the formula editor, under the Statistical group. Table > Summary can also spit out quantiles
If you want to investigate for outliers, sometimes the best way to identify them is with the eye. Like on a histogram or the outlier box plot. But, I'm sure there is perhaps an automated/quantitative way to do it. What is an outlier to one fitted distribution, may not be to another fitted distribution though.