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## Quantiles and Percentiles

This section explains how the Statistics and Machine Learning Toolbox™ functions `quantile` and `prctile` compute quantiles and percentiles.

The `prctile` function calculates the percentiles in a similar way as `quantile` calculates quantiles. The following steps in the computation of quantiles are also true for percentiles, given the fact that, for the same data sample, the quantile at the value Q is the same as the percentile at the value P = 100*Q.

1. `quantile` initially assigns the sorted values in `X` to the (0.5/n), (1.5/n), ..., ([n – 0.5]/n) quantiles. For example:

• For a data vector of six elements such as {6, 3, 2, 10, 8, 1}, the sorted elements {1, 2, 3, 6, 8, 10} respectively correspond to the (0.5/6), (1.5/6), (2.5/6), (3.5/6), (4.5/6), and (5.5/6) quantiles.

• For a data vector of five elements such as {2, 10, 5, 9, 13}, the sorted elements {2, 5, 9, 10, 13} respectively correspond to the 0.1, 0.3, 0.5, 0.7, and 0.9 quantiles.

The following figure illustrates this approach for data vector X = {2, 10, 5, 9, 13}. The first observation corresponds to the cumulative probability 1/5 = 0.2, the second observation corresponds to the cumulative probability 2/5 = 0.4, and so on. The step function in this figure shows these cumulative probabilities. `quantile` instead places the observations in midpoints, such that the first corresponds to 0.5/5 = 0.1, the second corresponds to 1.5/5 = 0.3, and so on, and then connects these midpoints. The red lines in the following figure connect the midpoints.

Assigning Observations to Quantiles By switching the axes, as the next figure, you can see the values of the variable X that correspond to the `p` quantiles.

Quantiles of X 2. `quantile` finds any quantiles between the data values using linear interpolation.

Linear interpolation uses linear polynomials to approximate a function f(x) and construct new data points within the range of a known set of data points. Algebraically, given the data points (x1, y1) and (x2, y2), where y1 = f(x1) and y2 = f(x2), linear interpolation finds y = f(x) for a given x between x1 and x2 as follows:

`$y=f\left(x\right)={y}_{1}+\frac{\left(x-{x}_{1}\right)}{\left({x}_{2}-{x}_{1}\right)}\left({y}_{2}-{y}_{1}\right).$`

Similarly, if the 1.5/n quantile is y1.5/n and the 2.5/n quantile is y2.5/n, then linear interpolation finds the 2.3/n quantile y2.3/n as

`${y}_{\frac{2.3}{n}}={y}_{\frac{1.5}{n}}+\frac{\left(\frac{2.3}{n}-\frac{1.5}{n}\right)}{\left(\frac{2.5}{n}-\frac{1.5}{n}\right)}\left({y}_{\frac{2.5}{n}}-{y}_{\frac{1.5}{n}}\right).$`

3. `quantile` assigns the first and last values of X to the quantiles for probabilities less than (0.5/n) and greater than ([n–0.5]/n), respectively.

 Langford, E. “Quartiles in Elementary Statistics”, Journal of Statistics Education. Vol. 14, No. 3, 2006.