Mean Absolute Deviation (MAD) is a statistical measure that quantifies the average absolute differences between each data point in a dataset and the mean of that dataset. It provides insights into the variability or dispersion of the data.
The formula to calculate MAD is:
\[ \text{MAD} = \frac{1}{n} \sum_{i=1}^{n} |x_i - \bar{x}| \]
Where:
- \( n \) is the number of data points in the dataset,
- \( x_i \) represents each individual data point,
- \( \bar{x} \) is the mean of the dataset,
- \( |x_i - \bar{x}| \) represents the absolute deviation of each data point from the mean.
MAD is often used in statistics to measure the spread of the data points, making it easier to understand how much the data varies from the average value. It is particularly useful because it does not give undue weight to outliers, as it considers the absolute values of the deviations.
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