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Add quantiles reference and config variables (#18314)

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Carlos Gomes 2021-06-30 03:42:37 -03:00 committed by GitHub
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source/_integrations/statistics.markdown
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@ -14,7 +14,7 @@ ha_platforms:
- sensor
---
The `statistics` sensor platform consumes the state from other sensors. It exports the `mean` value as state and the following values as attributes: `count`, `mean`, `median`, `stdev`, `variance`, `total`, `min_value`, `max_value`, `min_age`, `max_age`, `change`, `average_change` and `change_rate`. If the source is a binary sensor then only state changes are counted.
The `statistics` sensor platform consumes the state from other sensors. It exports the `mean` value as state and the following values as attributes: `count`, `mean`, `median`, `quantiles`, `stdev`, `variance`, `total`, `min_value`, `max_value`, `min_age`, `max_age`, `change`, `average_change` and `change_rate`. If the source is a binary sensor then only state changes are counted.
Assuming the [`recorder`](/integrations/recorder/) integration is running (either configured explicitly or as part of a meta-integration/dependency, e.g., [`default_config`](/integrations/default_config/), [`history`](/integrations/history/), etc.), historical sensor data is read from the database on startup and is available immediately after a restart of the platform. If the [`recorder`](/integrations/recorder/) integration is *not* running, it can take time for the sensor to start reporting data because some attribute calculations require more than one value.
@ -58,6 +58,16 @@ precision:
required: false
default: 2
type: integer
quantile_intervals:
description: Number of continuous intervals with equal probability. Value must be an integer higher than `1`. In addition, `quantiles` will be `unknown` unless the number of quantile intervals is *lower* than the number of data points (`count`). Set it to `4` for quartiles (default) or to `100` for percentiles, for example.
required: false
default: 4
type: integer
quantile_method:
description: Indicates whether quantiles are computed using the `exclusive` method (default) or `inclusive`. The `exclusive` method assumes the population data have more extreme values than the sample, and therefore, the part under the *i*-th of *m* sorted data points is computed as `i / (m + 1)`. The `inclusive` method assumes that the sample data includes the more extreme values from the population, and therefore, the part under the *i*-th of *m* sorted data points is computed as `(i - 1) / (m - 1)`.
required: false
default: exclusive
type: string
{% endconfiguration %}
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