# Stats

Factory functions for standard statistics strings.

- Source: [`aws-cloudwatch/lib/stats.ts`](https://github.com/aws/aws-cdk/blob/v2.275.0/packages/aws-cdk-lib/aws-cloudwatch/lib/stats.ts#L7)
- Type last changed: v2.54.0 · 7 Dec 2022

## Example

```ts
declare const dashboard: cloudwatch.Dashboard;
declare const executionCountMetric: cloudwatch.Metric;
declare const errorCountMetric: cloudwatch.Metric;

dashboard.addWidgets(new cloudwatch.GraphWidget({
  title: "Executions vs error rate",

  left: [executionCountMetric],

  right: [errorCountMetric.with({
    statistic: cloudwatch.Stats.AVERAGE,
    label: "Error rate",
    color: cloudwatch.Color.GREEN,
  })]
}));
```

## Initializer

```ts
new Stats()
```

## Properties

- `AVERAGE`: string (static) — The value of Sum / SampleCount during the specified period.

- `IQM`: string (static) — Interquartile mean (IQM) is the trimmed mean of the interquartile range, or the middle 50% of values.

  It is equivalent to `trimmedMean(25, 75)`.

- `MAXIMUM`: string (static) — The highest value observed during the specified period.

  You can use this value to determine high volumes of activity for your application.

- `MINIMUM`: string (static) — The lowest value observed during the specified period.

  You can use this value to determine low volumes of activity for your application.

- `SAMPLE_COUNT`: string (static) — The count (number) of data points used for the statistical calculation.

- `SUM`: string (static) — All values submitted for the matching metric added together.

  This statistic can be useful for determining the total volume of a metric.

## Methods

- `p(percentile: number): string` (static) — A shorter alias for `percentile()`.

- `percentile(percentile: number): string` (static) — Percentile indicates the relative standing of a value in a dataset.

  Percentiles help you get a better understanding of the distribution of your metric data.

  For example, `p(90)` is the 90th percentile and means that 90% of the data
  within the period is lower than this value and 10% of the data is higher
  than this value.

- `percentileRank(v1: number, v2?: number): string` (static) — Percentile rank (PR) is the percentage of values that meet a fixed threshold.

  - If two numbers are given, they define the lower and upper bounds in absolute values,
    respectively.
  - If one number is given, it defines the upper bound (the lower bound is assumed to
    be 0).

  For example, `percentileRank(300)` returns the percentage of data points that have a value of 300 or less.
  `percentileRank(100, 2000)` returns the percentage of data points that have a value between 100 and 2000.

- `pr(v1: number, v2?: number): string` (static) — Shorter alias for `percentileRank()`.

- `tc(p1: number, p2?: number): string` (static) — Shorter alias for `trimmedCount()`.

- `tm(p1: number, p2?: number): string` (static) — A shorter alias for `trimmedMean()`.

- `trimmedCount(p1: number, p2?: number): string` (static) — Trimmed count (TC) is the number of data points in the chosen range for a trimmed mean statistic.

  - If two numbers are given, they define the lower and upper bounds in percentages,
    respectively.
  - If one number is given, it defines the upper bound (the lower bound is assumed to
    be 0).

  For example, `tc(90)` returns the number of data points not including any
  data points that fall in the highest 10% of the values. `tc(10, 90)`
  returns the number of data points not including any data points that fall
  in the lowest 10% of the values and the highest 90% of the values.

- `trimmedMean(p1: number, p2?: number): string` (static) — Trimmed mean (TM) is the mean of all values that are between two specified boundaries.

  Values outside of the boundaries are ignored when the mean is calculated.
  You define the boundaries as one or two numbers between 0 and 100, up to 10
  decimal places. The numbers are percentages.

  - If two numbers are given, they define the lower and upper bounds in percentages,
    respectively.
  - If one number is given, it defines the upper bound (the lower bound is assumed to
    be 0).

  For example, `tm(90)` calculates the average after removing the 10% of data
  points with the highest values; `tm(10, 90)` calculates the average after removing the
  10% with the lowest and 10% with the highest values.

- `trimmedSum(p1: number, p2?: number): string` (static) — Trimmed sum (TS) is the sum of the values of data points in a chosen range for a trimmed mean statistic.

  It is equivalent to `(Trimmed Mean) * (Trimmed count)`.

  - If two numbers are given, they define the lower and upper bounds in percentages,
    respectively.
  - If one number is given, it defines the upper bound (the lower bound is assumed to
    be 0).

  For example, `ts(90)` returns the sum of the data points not including any
  data points that fall in the highest 10% of the values.  `ts(10, 90)`
  returns the sum of the data points not including any data points that fall
  in the lowest 10% of the values and the highest 90% of the values.

- `ts(p1: number, p2?: number): string` (static) — Shorter alias for `trimmedSum()`.

- `winsorizedMean(p1: number, p2?: number): string` (static) — Winsorized mean (WM) is similar to trimmed mean.

  However, with winsorized mean, the values that are outside the boundary are
  not ignored, but instead are considered to be equal to the value at the
  edge of the appropriate boundary.  After this normalization, the average is
  calculated. You define the boundaries as one or two numbers between 0 and
  100, up to 10 decimal places.

  - If two numbers are given, they define the lower and upper bounds in percentages,
    respectively.
  - If one number is given, it defines the upper bound (the lower bound is assumed to
    be 0).

  For example, `tm(90)` calculates the average after removing the 10% of data
  points with the highest values; `tm(10, 90)` calculates the average after removing the
  10% with the lowest and 10% with the highest values.

  For example, `wm(90)` calculates the average while treating the 10% of the
  highest values to be equal to the value at the 90th percentile.
  `wm(10, 90)` calculates the average while treaing the bottom 10% and the
  top 10% of values to be equal to the boundary values.

- `wm(p1: number, p2?: number): string` (static) — A shorter alias for `winsorizedMean()`.