Mahalanobis Distances in SPSS – A Quick Guide By Ruben Geert van den Berg under Statistics A-Z & SPSS Blog Summary Mahalanobis Distances - Basic Reasoning Mahalanobis Distances - Formula and Properties Finding Mahalanobis Distances in SPSS Critical Values Table for Mahalanobis Distances Mahalanobis Distances & Missing Values Summary In SPSS, you can compute (squared) Mahalanobis distances as ...
Mahalanobis' distance (MD) is a statistical measure of the extent to which cases are multivariate outliers, based on a chi-square distribution, assessed using p < .001. The critical chi-square values for 2 to 10 degrees of freedom at a critical alpha of .001 are shown below. A maximum MD larger than the critical chi-square value for df = k (the number of predictor variables in the model) at a ...
The Mahalanobis distance is a well-known criterion which may be used for detecting outliers in multivariate data. However, there are some discrepancies about which critical values are suitable for this purpose.
Key Takeaways Mahalanobis distance accounts for variable correlations; Euclidean distance does not. Under multivariate normality, squared distances follow a chi-squared distribution with p degrees of freedom. Observations exceeding the chi-squared critical value at a chosen alpha are flagged as potential outliers.
Say I work out the mahalanobis distance 'D' to measure the separation between two objects (which aren't normally distributed). Say I now want to use 'D' against some critical values to decide if it's an outlier or not. I've read that using Chi-Square Distribution is one way, using N-1 degree of freedom and converting the distance to Chi-square p values. However, it states that because isn't ...