Update README.md

README.md

@@ -308,7 +308,7 @@ Best steps to prevent anomalies is to implement policies or checks that can catc
 Statistically based anomaly detection uses this knowledge to discover outliers. A dataset can be standardized by taking the z-score of each point. A z-score is a measure of how many standard deviations a data point is away from the mean of the data. Any data-point that has a z-score higher than 3 is an outlier, and likely to be an anomaly. As the z-score increases above 3, points become more obviously anomalous. A z-score is calculated using the following equation. A box-plot is perfect for this application.
 
 #### Metric method
-Judging by the number of publications, metric methods are the most popular methods among researchers. They postulate the existence of a certain metric in the space of objects, which helps to find anomalies. Intuitively, the anomaly has few neighbors in the instannce space, and a typical point has many. Therefore, a good measure of anomalies can be, for example, the «distance to the k-th neighbor». (See method: [Local Outlier Factor](https://en.wikipedia.org/wiki/Local_outlier_factor)). Specific metrics are used here, for example [Mahalonobis distance] (https://en.wikipedia.org/wiki/Mahalanobis_distance). Mahalonobis distance is a measure of distance between vectors of random variables, generalizing the concept of Euclidean distance. Using Mahalonobis distance, it is possible to determine the similarity of unknown and known samples. It differs from Euclidean distance in that it takes into account correlations between variables and is scale invariant.
+Judging by the number of publications, metric methods are the most popular methods among researchers. They postulate the existence of a certain metric in the space of objects, which helps to find anomalies. Intuitively, the anomaly has few neighbors in the instannce space, and a typical point has many. Therefore, a good measure of anomalies can be, for example, the «distance to the k-th neighbor». (See method: [Local Outlier Factor](https://en.wikipedia.org/wiki/Local_outlier_factor)). Specific metrics are used here, for example [Mahalonobis distance](https://en.wikipedia.org/wiki/Mahalanobis_distance). Mahalonobis distance is a measure of distance between vectors of random variables, generalizing the concept of Euclidean distance. Using Mahalonobis distance, it is possible to determine the similarity of unknown and known samples. It differs from Euclidean distance in that it takes into account correlations between variables and is scale invariant.
 ![alt text](images/metrical-methods.png)
 
 The most common form of clustering-based anomaly detection is done with prototype-based clustering.