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It is also known as the lower quartile. The second quartile (Q 2) is the median of a data set; thus 50% of the data lies below this point. The third quartile (Q 3) is the 75th percentile where lowest 75% data is below this point. It is known as the upper quartile, as 75% of the data lies below this point. [1]
The lower quartile, Q 1, is a number such that integral of the PDF from -∞ to Q 1 equals 0.25, while the upper quartile, Q 3, is such a number that the integral from -∞ to Q 3 equals 0.75; in terms of the CDF, the quartiles can be defined as follows: = (),
upper whisker top end The middle three values – the lower quartile , median , and upper quartile – are the usual statistics from the five-number summary and are the standard values for the box in a box plot .
Splitting the observations either side of the median gives two groups of four observations. The median of the first group is the lower or first quartile, and is equal to (0 + 1)/2 = 0.5. The median of the second group is the upper or third quartile, and is equal to (27 + 61)/2 = 44. The smallest and largest observations are 0 and 63.
Third quartile (Q 3 or 75th percentile): also known as the upper quartile q n (0.75), it is the median of the upper half of the dataset. [ 7 ] In addition to the minimum and maximum values used to construct a box-plot, another important element that can also be employed to obtain a box-plot is the interquartile range (IQR), as denoted below:
A measure of statistical dispersion is a nonnegative real number that is zero if all the data are the same and increases as the data become more diverse.. Most measures of dispersion have the same units as the quantity being measured.
Upper and lower probabilities are representations of imprecise probability. Whereas probability theory uses a single number, the probability , to describe how likely an event is to occur, this method uses two numbers: the upper probability of the event and the lower probability of the event.
A Pearson density p is defined to be any valid solution to the differential equation (cf. Pearson 1895, p. 381) ′ () + + + + = ()with: =, = = +, =. According to Ord, [3] Pearson devised the underlying form of Equation (1) on the basis of, firstly, the formula for the derivative of the logarithm of the density function of the normal distribution (which gives a linear function) and, secondly ...