The Lower Quartile: We can also call the lower quartile as the first quartile which falls below 25 percent of the scores. It is seen that most of the scores are much greater or equal to the value and half are less.
The Median: The median represents the midpoint of a data which can be shown using the line that divides the box into two halves (it is sometimes also called the second quartile). The Maximum Score: The maximum score is the highest score after excluding the outliers. The Minimum Score: The minimum score is the lowest score after excluding the outliers. A box plot is like a chart that we often use in exploratory data analysis.īox plots have a five-number summary of a set of data that includes the minimum score, first quartile (lower), median, third quartile (upper), and the maximum score.Īlthough it may seem that a box plot is primitive if compared to a histogram or a density plot, they have an advantage of occupying less space, which is quite useful when comparing distributions between many groups or datasets. Box plots are drawn in two ways, i.e., we can choose to draw it either horizontally or vertically. The gaps between the different parts of the box indicate the degree of dispersion (spread) and the skewness present in the data, along with the show outliers. And this feature of the Box plot actually helps to display a variation of a statistical population in the samples where it does not make any assumptions about the underlying statistical distribution. Being non-parametric is one of the features of the Box plot. Whiskers are used to indicate variability outside the upper and the lower quartiles. Box plots extend its lines from the boxes which are normally called whiskers. A box plot is a graph that offers us a much firm indication or idea about how the values in the data should be spread. This may arise a predictable question that is “what is a box and a whiskers plot?” the question can be answered satisfactorily. This demand can be fulfilled by a box and whiskers plot. Information on the variability or the dispersion of the data demands a much more concrete foundation. Sometimes we need more elaborated details in various distributions or datasets that may not be fulfilled by the measures of any central tendency like mean, median, and mode.
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