>>> # Example from https://statistics.laerd.com/statistical-guides/spearmans-rank-order-correlation-statistical-guide.php
>>> eng = [56, 75, 45, 71, 61, 64, 58, 80, 76, 61]
>>> math = [66, 70, 40, 60, 65, 56, 59, 77, 67, 63]
>>> # Pearson correlation reports the strength of a linear relationship
>>> correlation(math, eng)
0.8005386765540159
>>> # Spearman correlation reports strength of a monotonic relationship
>>> correlation(math, eng, ranked=True)
0.6686960980480711
The code would be mostly unchanged. If ranked is true, then the data is replaced by rankings using a helper function
Spearman correlation would be easily supported by adding a
rankedoption tostatistics.correlation(). It is appropriate for ordinal data or for continuous data that doesn't meet the linear proportionality assumption required for Pearson correlation.The code would be mostly unchanged. If
rankedis true, then the data is replaced by rankings using a helper function