Histogram Matching
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Histogram Matching    
Four histograms are shown. They are identified by their shapes. (“LaPlace” is a technical term for a specific symmetric distribution that has a sharp peak and relatively long “tails.” The histogram above is a good example.) All are based on data sets with n = 80 observations. For each of the distributions, compare the measures below. Provide values where you can, although especially for IQR and Standard Deviation it is more important to rank the distributions from smallest to largest. “About the same” is appropriate for distinctions that cannot be made from the histograms alone. Let’s say “within 15% of each other” is equivalent to “about the same.” Mean Median
Range IQR
Standard Deviation The data are online at www.oswego.edu/~srp/stats/histmat1.txt. For each column obtain the histogram and descriptive statistics. Your histograms may differ from those shown above because special care was taken to place the above histograms all on exactly the same scale. This care is useful for best making comparisons by visual inspection. Enter the mean for each distribution in the table below. These are far too close to be called anything but “about the same” based on visual inspection of the histograms. The means all differ by less than 0.17, which is less than 2% of any of the means – which are all around 10. There’s no way the means can be distinguished by histogram inspection only. Enter the median for each in the table below. Since all the distributions are fairly symmetric, we expect medians to be close to means. They are, and are all “about the same” for similar reasons as above. Enter the range for each distribution in the table below. “About the same for all” is the only possible answer from histogram inspection alone. They differ slightly, but the biggest difference between any two ranges is less than 7%. Enter the IQR for each distribution in the table below. These are substantially different. While it is difficult to guess precise values from the histograms alone, you should be able to rank the IQRs from low to high on the basis of the histograms. Enter the Standard Deviation for each in the table below. These are substantially different. You should be able to rank these as well (but again, guessing values from histograms is not easy). How do the rankings of IQRs compare to those of Standard Deviations? For each data set, compute Standard Deviation IQR. These are considerably different, which reinforces a conclusion that while both IQR and Standard Deviation measure the same thing (they both measure _____________), they do so in different ways. In fact, a rule of thumb is that for bell-shaped distributions this ratio tends to be about ¾. For most distributions this ratio is between ½ and 1; however, you can see that it is possible for the IQR to be smaller than the standard deviation (ratio above 1), and also for the standard deviation to be less than ½ the IQR.
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