Returns the sample Gini coefficient, a measure of statistical dispersion.

## Syntax

**Gini**(**X**)

- X
- is the input data sample (must be non-negative) (a one-dimensional array of cells (e.g., rows or columns)).

## Remarks

- A low Gini coefficient indicates a more equal distribution, with 0 corresponding to complete equality. While higher Gini coefficients indicate more unequal distributions, with 1 corresponding to complete inequality.
- The input data series may include missing values (e.g., #N/A, #VALUE!, #NUM!, empty cell), but they will not be included in the calculations.
- The values in the input data series must be non-negative.
- The Gini coefficient is computed as follows: $$G(S)=1-\frac{2}{n-1}\left ( n-\frac{\sum_{i=1}^{n}iy_i}{\sum_{i=1}^{n}y_i} \right )$$ Where:
- $h$ is the input data series ($h$) arranged in descending order, so that $y_i\leq y_{i+1}$.
- $n$ is the number of non-missing values in the input time series data sample.

- The Gini coefficient value can range from 0 to 1 and is half the relative mean difference (RMD).
- $G(S)$ is a consistent estimator of the population Gini coefficient but is generally unbiased (except when the population mean is known).
- Developed by the Italian statistician Corrado Gini in 1912, the Gini coefficient is commonly used as a measure of comparative income or wealth. Where zero (0) corresponds to complete equality and one (1) to complete equality.

## Files Examples

## Related Links

## References

- Hamilton, J .D.; Time Series Analysis, Princeton University Press (1994), ISBN 0-691-04289-6.
- Tsay, Ruey S.; Analysis of Financial Time Series John Wiley & SONS. (2005), ISBN 0-471-690740.

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