The similarity coefficients proposed by the calculations from the qualitative data are as follows: Cooccurrence, Percent agreement. Similarities and dissimilarities for qualitative data Sokal & Michener's coefficient (simple matching coefficient),.Dice coefficient (also known as the Sorensen coefficient),.The similarity and dissimilarity (per simple transformation) coefficients proposed by the calculations from the binary data are as follows: Similarities and dissimilarities for binary data The dissimilarity coefficients proposed by the calculations from the quantitative data are as follows: The similarity coefficients proposed by the calculations from the quantitative data are as follows: Cosine, Covariance (n-1), Covariance (n), Inertia, Gower coefficient, Kendall correlation coefficient, Pearson correlation coefficient, Spearman correlation coefficient. The indexes offered depend on the nature of the data: Similarities and dissimilarities for quantitative data The proximity between two objects is measured by measuring at what point they are similar (similarity) or dissimilar (dissimilarity).
#SIMILARITY MATRIX HOW TO XLSTAT SERIES#
This tool offers a large number of proximity measurements between a series of objects whether they are in rows (usually the observations) or in columns (usually the variables). XLSTAT helps you to explain the relationships between variables in term of similarities and dissimilarities by measuring their proximity. XLSTAT - Correlations and Similarity/Dissimilarity Matrix Similarity and dissimilarity tool in XLSTAT