Graphical-statistics-Open-Access-Articles
A picture is worth a thousand words, or numbers, and there is no better way of getting a 'feel' for the data than to display them in a figure or graph. The general principle should be to convey as much
information as possible in the figure, with the constraint that the reader is not overwhelmed by too much detail. The simplest method of conveying as much
information as possible is to show all of the data and this can be conveniently carried out using a Dot plot. Data on birth weight and type of delivery are shown in Figure 1 as a Dot plot. This method of presentation retains the individual subject values and clearly demonstrates differences between the groups in a readily appreciated manner. An additional advantage is that any outliers will be detected by such a plot. However, such presentation is not usually practical with large numbers of subjects in each group because the dots will obscure the details of the distribution. The patterns may be revealed in a large data set of a numerically continuous variable by forming a histogram. This is constructed by first dividing up the range of the variable into several non-overlapping and equal intervals (also called “classes” or “bins”), then counting the number of observations in each. A histogram for all the 98 birth weights in the Simpson (2004) data is shown in Figure 2. The area of each histogram block is proportional to the number of subjects in the particular birth-weight category concentration group. Thus, the total area in the histogram blocks represents the total number of volunteers. Relative frequency histograms, where the y-axis shows the proportion of the observations in each bin rather than an absolute number, allow comparison between histograms made up of different numbers of observations which may be useful when studies are compared.
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