java.lang.Object
- com.google.common.math.PairedStats

All Implemented Interfaces:

Serializable
```
@GwtIncompatible
public final class PairedStats
extends Object
implements Serializable
```
An immutable value object capturing some basic statistics about a collection of paired double values (e.g. points on a plane). Build instances with PairedStatsAccumulator.snapshot().

Since:

20.0

Author:

Pete Gillin

See Also:

Serialized Form

Method Summary

All Methods Static Methods Instance Methods Concrete Methods
Modifier and Type	Method	Description
`long`	`count()`	Returns the number of pairs in the dataset.
`boolean`	`equals(Object obj)`
`static PairedStats`	`fromByteArray(byte[] byteArray)`	Creates a `PairedStats` instance from the given byte representation which was obtained by `toByteArray()`.
`int`	`hashCode()`
`LinearTransformation`	`leastSquaresFit()`	Returns a linear transformation giving the best fit to the data according to Ordinary Least Squares linear regression of `y` as a function of `x`.
`double`	`pearsonsCorrelationCoefficient()`	Returns the Pearson's or product-moment correlation coefficient of the values.
`double`	`populationCovariance()`	Returns the population covariance of the values.
`double`	`sampleCovariance()`	Returns the sample covariance of the values.
`byte[]`	`toByteArray()`	Gets a byte array representation of this instance.
`String`	`toString()`
`Stats`	`xStats()`	Returns the statistics on the `x` values alone.
`Stats`	`yStats()`	Returns the statistics on the `y` values alone.

Methods inherited from class java.lang.Object
clone, finalize, getClass, notify, notifyAll, wait, wait, wait

- Method Detail
  - count
```
public long count()
```
    Returns the number of pairs in the dataset.
  - xStats
```
public Stats xStats()
```
    Returns the statistics on the x values alone.
  - yStats
```
public Stats yStats()
```
    Returns the statistics on the y values alone.
  - populationCovariance
```
public double populationCovariance()
```
    Returns the population covariance of the values. The count must be non-zero.
    This is guaranteed to return zero if the dataset contains a single pair of finite values. It is not guaranteed to return zero when the dataset consists of the same pair of values multiple times, due to numerical errors.
    Non-finite values
    
    If the dataset contains any non-finite values (Double.POSITIVE_INFINITY, Double.NEGATIVE_INFINITY, or Double.NaN) then the result is Double.NaN.
    
    Throws:
    
    IllegalStateException - if the dataset is empty
  - sampleCovariance
```
public double sampleCovariance()
```
    Returns the sample covariance of the values. The count must be greater than one.
    This is not guaranteed to return zero when the dataset consists of the same pair of values multiple times, due to numerical errors.
    Non-finite values
    
    If the dataset contains any non-finite values (Double.POSITIVE_INFINITY, Double.NEGATIVE_INFINITY, or Double.NaN) then the result is Double.NaN.
    
    Throws:
    
    IllegalStateException - if the dataset is empty or contains a single pair of values
  - pearsonsCorrelationCoefficient
```
public double pearsonsCorrelationCoefficient()
```
    Returns the Pearson's or product-moment correlation coefficient of the values. The count must greater than one, and the x and y values must both have non-zero population variance (i.e. xStats().populationVariance() > 0.0 && yStats().populationVariance() > 0.0). The result is not guaranteed to be exactly +/-1 even when the data are perfectly (anti-)correlated, due to numerical errors. However, it is guaranteed to be in the inclusive range [-1, +1].
    Non-finite values
    
    If the dataset contains any non-finite values (Double.POSITIVE_INFINITY, Double.NEGATIVE_INFINITY, or Double.NaN) then the result is Double.NaN.
    
    Throws:
    
    IllegalStateException - if the dataset is empty or contains a single pair of values, or either the x and y dataset has zero population variance
  - leastSquaresFit
```
public LinearTransformation leastSquaresFit()
```
    Returns a linear transformation giving the best fit to the data according to Ordinary Least Squares linear regression of y as a function of x. The count must be greater than one, and either the x or y data must have a non-zero population variance (i.e. xStats().populationVariance() > 0.0 || yStats().populationVariance() > 0.0). The result is guaranteed to be horizontal if there is variance in the x data but not the y data, and vertical if there is variance in the y data but not the x data.
    This fit minimizes the root-mean-square error in y as a function of x. This error is defined as the square root of the mean of the squares of the differences between the actual y values of the data and the values predicted by the fit for the x values (i.e. it is the square root of the mean of the squares of the vertical distances between the data points and the best fit line). For this fit, this error is a fraction sqrt(1 - R*R) of the population standard deviation of y, where R is the Pearson's correlation coefficient (as given by pearsonsCorrelationCoefficient()).
    The corresponding root-mean-square error in x as a function of y is a fraction sqrt(1/(R*R) - 1) of the population standard deviation of x. This fit does not normally minimize that error: to do that, you should swap the roles of x and y.
    Non-finite values
    
    If the dataset contains any non-finite values (Double.POSITIVE_INFINITY, Double.NEGATIVE_INFINITY, or Double.NaN) then the result is LinearTransformation.forNaN().
    
    Throws:
    
    IllegalStateException - if the dataset is empty or contains a single pair of values, or both the x and y dataset must have zero population variance
  - equals
```
public boolean equals(@CheckForNull
                      Object obj)
```
    Note: This tests exact equality of the calculated statistics, including the floating point values. Two instances are guaranteed to be considered equal if one is copied from the other using second = new PairedStatsAccumulator().addAll(first).snapshot(), if both were obtained by calling snapshot() on the same PairedStatsAccumulator without adding any values in between the two calls, or if one is obtained from the other after round-tripping through java serialization. However, floating point rounding errors mean that it may be false for some instances where the statistics are mathematically equal, including instances constructed from the same values in a different order... or (in the general case) even in the same order. (It is guaranteed to return true for instances constructed from the same values in the same order if strictfp is in effect, or if the system architecture guarantees strictfp-like semantics.)
    
    Overrides:
    
    equals in class Object
  - hashCode
```
public int hashCode()
```
    Note: This hash code is consistent with exact equality of the calculated statistics, including the floating point values. See the note on equals(java.lang.Object) for details.
    
    Overrides:
    
    hashCode in class Object
  - toString
```
public String toString()
```
    Overrides:
    
    toString in class Object
  - toByteArray
```
public byte[] toByteArray()
```
    Gets a byte array representation of this instance.
    Note: No guarantees are made regarding stability of the representation between versions.
  - fromByteArray
```
public static PairedStats fromByteArray(byte[] byteArray)
```
    Creates a PairedStats instance from the given byte representation which was obtained by toByteArray().
    Note: No guarantees are made regarding stability of the representation between versions.

Class PairedStats

Method Summary

Methods inherited from class java.lang.Object

Method Detail

count

xStats

yStats

populationCovariance

Non-finite values

sampleCovariance

Non-finite values

pearsonsCorrelationCoefficient

Non-finite values

leastSquaresFit

Non-finite values

equals

hashCode

toString

toByteArray

fromByteArray