![]() Such a plot is illustrated in Figure 3 for the birth weight and type of delivery from Simpson (2004).įigure 3 Box-Whisker plot of birth weight of babies by method of delivery (data from Simpson, 2004) If the number of points is large, a Dot plot can be replaced by a box-whisker plot which is more compact than the corresponding histogram. However, because data are grouped into intervals, exact values of each observation cannot be determined. Histograms with bins of unequal interval length can be constructed but they are usually best avoided.Īdvantages of histograms include the ability to visualise the shape of the frequency distribution and to demonstrate central tendency. It is usual to choose between 5 and 15 intervals, but the correct choice will be based partly on a subjective impression of the resulting histogram. Too few intervals and much important information may be smoothed out too many intervals and the underlying shape will be obscured by a mass of confusing detail. The choice of the number of intervals is important. ![]() 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.įigure 2 Histogram of birth weight of 98 babies (data from Simpson 2004) ![]() Thus, the total area in the histogram blocks represents the total number of volunteers. The area of each histogram block is proportional to the number of subjects in the particular birth-weight category concentration group. A histogram for all the 98 birth weights in the Simpson (2004) data is shown in Figure 2. 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. The patterns may be revealed in a large data set of a numerically continuous variable by forming a histogram. 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.įigure 1 Dot plot showing birth weight of 98 babies by type of delivery with the medians shown by '+' (data from Simpson 2004) An additional advantage is that any outliers will be detected by such a plot. This method of presentation retains the individual subject values and clearly demonstrates differences between the groups in a readily appreciated manner. 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.ĭata on birth weight and type of delivery are shown in Figure 1 as a Dot plot. 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. 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.
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