Statistics tools

class pycalib.stats.TestResult(statistic, p_value)

Methods

count(value, /)	Return number of occurrences of value.
index(value[, start, stop])	Return first index of value.

p_value

Alias for field number 1

statistic

Alias for field number 0

pycalib.stats.compute_friedmanchisquare(table: DataFrame) → TestResult

Compute Friedman test for repeated samples

Example:

• n wine judges each rate k different wines. Are any of the k wines

ranked consistently higher or lower than the others?

Our Calibration case:

• n datasets each rate k different calibration methods. Are any of the

k calibration methods ranked consistently higher or lower than the others?

This will output a statistic and a p-value SciPy does the following:

• k: is the number of parameters passed to the function

• n: is the length of each array passed to the function

The two options for the given table are:

• k is the datasets: table[‘mean’].values.tolist()

• k is the calibration methods: table[‘mean’].T.values.tolist()