# Statistics¶

## The base class Statistics¶

Abstract class for the estimation of statistics from a dataset.

### Overview¶

The abstract Statistics class implements the concept of statistics library. It is enriched by the EmpiricalStatistics and ParametricStatistics.

### Construction¶

A Statistics object is built from a Dataset and optionally variables names. In this case, statistics are only computed for these variables. Otherwise, statistics are computed for all the variable available in the dataset. Lastly, the user can give a name to its Statistics object. By default, this name is the concatenation of the name of the class overloading Statistics and the name of the Dataset.

### Capabilities¶

A Statistics returns standard descriptive and statistical measures for the different variables:

Classes:

 Statistics(dataset[, variables_names, name]) Abstract class to interface a statistics library.
class gemseo.uncertainty.statistics.statistics.Statistics(dataset, variables_names=None, name=None)[source]

Abstract class to interface a statistics library.

dataset

The dataset.

Type

Dataset

n_samples

The number of samples.

Type

int

n_variables

The number of variables.

Type

int

name

The name of the object.

Type

str

Initialize self. See help(type(self)) for accurate signature.

Parameters
• dataset (Dataset,) – A dataset.

• variables_names (Optional[Iterable[str]]) –

The variables of interest. Default: consider all the variables available in the dataset.

By default it is set to None.

• name (Optional[str]) –

A name for the object. Default: use the concatenation of the class and dataset names.

By default it is set to None.

Return type

None

Methods:

 Compute the A-value. Compute the B-value. compute_expression(variable, function[, ...]) Return the expression of a statistical function applied to a variable. Compute the maximum. Compute the mean. compute_mean_std(std_factor) Compute mean + std_factor * std. Compute the median. Compute the minimum. compute_moment(order) Compute the n-th moment. compute_percentile(order) Compute the n-th percentile. compute_probability(thresh[, greater]) Compute the probability related to a threshold. Compute the quantile related to a probability. compute_quartile(order) Compute the n-th quartile. Compute the range. Compute the standard deviation. compute_tolerance_interval(coverage[, ...]) Compute a tolerance interval (TI) for a given coverage level. Compute the variance.
compute_a_value()[source]

Compute the A-value.

Returns

The A-value of the different variables.

Return type

Dict[str, numpy.ndarray]

compute_b_value()[source]

Compute the B-value.

Returns

The B-value of the different variables.

Return type

Dict[str, numpy.ndarray]

classmethod compute_expression(variable, function, show_name=False, **options)[source]

Return the expression of a statistical function applied to a variable.

Parameters
• variable (str) – The name of the variable.

• function (str) – The name of the function.

• show_name (bool) –

If True, show name. Otherwise, only show value.

By default it is set to False.

• **options – The options passed to the statistical function.

Returns

The expression of the statistical function applied to the variable.

Return type

str

compute_maximum()[source]

Compute the maximum.

Returns

The maximum of the different variables.

Return type

Dict[str, numpy.ndarray]

compute_mean()[source]

Compute the mean.

Returns

The mean of the different variables.

Return type

Dict[str, numpy.ndarray]

compute_mean_std(std_factor)[source]

Compute mean + std_factor * std.

Returns

mean + std_factor * std for the different variables.

Parameters

std_factor (float) –

Return type

Dict[str, numpy.ndarray]

compute_median()[source]

Compute the median.

Returns

The median of the different variables.

Return type

Dict[str, numpy.ndarray]

compute_minimum()[source]

Compute the minimum.

Returns

The minimum of the different variables.

Return type

Dict[str, numpy.ndarray]

compute_moment(order)[source]

Compute the n-th moment.

Parameters

order (int) – The order of a moment.

Returns

The moment of the different variables.

Return type

Dict[str, numpy.ndarray]

compute_percentile(order)[source]

Compute the n-th percentile.

Parameters

order (int) – The order of the percentile. Either 0, 1, 2, … or 100.

Returns

The percentile of the different variables.

Return type

Dict[str, numpy.ndarray]

compute_probability(thresh, greater=True)[source]

Compute the probability related to a threshold.

Parameters
• thresh (float) – A threshold.

• greater (bool) –

The type of probability. If True, compute the probability of exceeding the threshold. Otherwise, compute the opposite.

By default it is set to True.

Returns

The probability of the different variables

Return type

Dict[str, numpy.ndarray]

compute_quantile(prob)[source]

Compute the quantile related to a probability.

Parameters

prob (float) – A probability between 0 and 1.

Returns

The quantile of the different variables.

Return type

Dict[str, numpy.ndarray]

compute_quartile(order)[source]

Compute the n-th quartile.

Parameters

order (int) – The order of the quartile. Either 1, 2 or 3.

Returns

The quartile of the different variables.

Return type

Dict[str, numpy.ndarray]

compute_range()[source]

Compute the range.

Returns

The range of the different variables.

Return type

Dict[str, numpy.ndarray]

compute_standard_deviation()[source]

Compute the standard deviation.

Returns

The standard deviation of the different variables.

Return type

Dict[str, numpy.ndarray]

compute_tolerance_interval(coverage, confidence=0.95, side=ToleranceIntervalSide.BOTH)[source]

Compute a tolerance interval (TI) for a given coverage level.

This coverage level is the minimum percentage of belonging to the TI. The tolerance interval is computed with a confidence level and can be either lower-sided, upper-sided or both-sided.

Parameters
• coverage (float) – A minimum percentage of belonging to the TI.

• confidence (float) –

A level of confidence in [0,1].

By default it is set to 0.95.

• The type of the tolerance interval characterized by its sides of interest, either a lower-sided tolerance interval $$[a, +\infty[$$, an upper-sided tolerance interval $$]-\infty, b]$$, or a two-sided tolerance interval $$[c, d]$$.

By default it is set to BOTH.

Returns

The tolerance limits of the different variables.

Return type

Dict[str, Tuple[numpy.ndarray, numpy.ndarray]]

compute_variance()[source]

Compute the variance.

Returns

The variance of the different variables.

Return type

Dict[str, numpy.ndarray]