maths.spearman_rank_correlation_coefficient

Functions

assign_ranks(→ list[int])	Assigns ranks to elements in the array.
calculate_spearman_rank_correlation(→ float)	Calculates Spearman's rank correlation coefficient.

Module Contents

maths.spearman_rank_correlation_coefficient.assign_ranks(data: collections.abc.Sequence[float]) → list[int]

Assigns ranks to elements in the array.

Parameters:

data – List of floats.

Returns:

List of ints representing the ranks.

Example: >>> assign_ranks([3.2, 1.5, 4.0, 2.7, 5.1]) [3, 1, 4, 2, 5]

>>> assign_ranks([10.5, 8.1, 12.4, 9.3, 11.0])
[3, 1, 5, 2, 4]

maths.spearman_rank_correlation_coefficient.calculate_spearman_rank_correlation(variable_1: collections.abc.Sequence[float], variable_2: collections.abc.Sequence[float]) → float

Calculates Spearman’s rank correlation coefficient.

Parameters:

• variable_1 – List of floats representing the first variable.

• variable_2 – List of floats representing the second variable.

Returns:

Spearman’s rank correlation coefficient.

Example Usage:

>>> x = [1, 2, 3, 4, 5]
>>> y = [5, 4, 3, 2, 1]
>>> calculate_spearman_rank_correlation(x, y)
-1.0

>>> x = [1, 2, 3, 4, 5]
>>> y = [2, 4, 6, 8, 10]
>>> calculate_spearman_rank_correlation(x, y)
1.0

>>> x = [1, 2, 3, 4, 5]
>>> y = [5, 1, 2, 9, 5]
>>> calculate_spearman_rank_correlation(x, y)
0.6