![]() ![]() On the downside, a box plot’s simplicity also sets limitations on the density of data that it can show. It is easy to see where the main bulk of the data is, and make that comparison between different groups. They are built to provide high-level information at a glance, offering general information about a group of data’s symmetry, skew, variance, and outliers. The box and whiskers plot provides a cleaner representation of the general trend of the data, compared to the equivalent line chart.īox plots are used to show distributions of numeric data values, especially when you want to compare them between multiple groups. Points show days with outlier download counts: there were two days in June and one day in October with low downloads compared to other days in the month. There also appears to be a slight decrease in median downloads in November and December. From this plot, we can see that downloads increased gradually from about 75 per day in January to about 95 per day in August. The example box plot above shows daily downloads for a fictional digital app, grouped together by month. ![]() Lines extend from each box to capture the range of the remaining data, with dots placed past the line edges to indicate outliers. Box limits indicate the range of the central 50% of the data, with a central line marking the median value. But 10.2 is fully below the lower outer fence, so 10.2 would be an extreme value.A box plot (aka box and whisker plot) uses boxes and lines to depict the distributions of one or more groups of numeric data. Since 16.4 is right on the upper outer fence, this would be considered to be only an outlier, not an extreme value. Then the outliers will be the numbers that are between one and two steps from the hinges, and extreme value will be the numbers that are more than two steps from the hinges. The outliers (marked with asterisks or open dots) are between the inner and outer fences, and the extreme values (marked with whichever symbol you didn't use for the outliers) are outside the outer fences.īy the way, your book may refer to the value of " 1.5×IQR " as being a "step". If your assignment is having you consider not only outliers but also "extreme" values, then the values for Q 1 − 1.5×IQR and Q 3 + 1.5×IQR are the "inner" fences and the values for Q 1 − 3×IQR and Q 3 + 3×IQR are the "outer" fences. The values for Q 1 − 1.5×IQR and Q 3 + 1.5×IQR are the "fences" that mark off the so-called reasonable values from the outlier values. Who knows? But whatever their cause, the outliers are those points that don't seem to fit. Maybe you bumped the weigh-scale when you were making that one measurement, or maybe your lab partner is an idiot and you should never have let him touch any of the equipment. That is, if a data point is below Q 1 − 1.5×IQR or above Q 3 + 1.5×IQR, it is viewed as being too far from the central values to be reasonable. An outlier is any value that lies more than one and a half times the length of the box from either end of the box. The IQR is the length of the box in your box-and-whisker plot. How do outliers relate to the Inter-Quartile Range? These "too far away" points are called outliers, because they lie outside the range in which we expect them. The IQR tells how spread out the middle (or the bulk of the) values are it can also be used to tell when some of the other values are, in some sense, "too far" from the central value(s). Statistics assumes that your values are clustered around some central value. They are points way off to one end or the other, which are discarded as being "noise", a mismeasurement, or some other sort of error. Outliers are data points which are regarded as being too far from the bulk of the data points to be valid.
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