org.scalameter.utils

Statistics

object Statistics

Standard statistics utilities.

Note: significance level alpha is equal to 1 - confidenceLevel. If you want to be sure that 2 sets of measurements do not differ with 90 percent probability, then the significance level alpha should be set to 0.1. In this example, the confidence level is 0.9, and the significance level is 0.1.

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  1. case class ANOVAFTest(alternatives: Seq[Seq[Double]], alpha: Double) extends Test with Product with Serializable

    ANOVA separates the total variation in a set of measurements into a component due to random fluctuations in the measurements and a component due to the actual differences among the alternatives.

  2. case class CoVTest(measurements: Seq[Double], threshold: Double) extends Test with Product with Serializable

    Compares the coefficient of variance to some threshold value.

  3. case class ConfidenceIntervalTest(strict: Boolean, alt1: Seq[Double], alt2: Seq[Double], alpha: Double) extends Standard2WayTest with Product with Serializable

    Compares two alternative sets of measurements given a significance level alpha.

  4. case class OverlapTest(alt1: Seq[Double], alt2: Seq[Double], alpha: Double, noiseMagnitude: Double) extends Standard2WayTest with Product with Serializable

    Computes the confidence interval of the two alternatives.

  5. abstract class Standard2WayTest extends Test

  6. trait Test extends AnyRef

Value Members

  1. final def !=(arg0: Any): Boolean

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  2. final def ##(): Int

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  3. final def ==(arg0: Any): Boolean

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  4. def CoV(measurements: Seq[Double]): Double

  5. def SSA(alternatives: Seq[Seq[Double]]): Double

    Computes sum-of-squares due to differences between alternatives.

  6. def SSE(alternatives: Seq[Seq[Double]]): Double

    Computes sum-of-squares due to errors in measurements.

  7. final def asInstanceOf[T0]: T0

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  8. def clamp(x: Double, below: Double, above: Double): Double

  9. def clone(): AnyRef

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  10. def confidenceInterval(seq: Seq[Double], alpha: Double): (Double, Double)

    Let Y = (Y_1, .

    Let Y = (Y_1, ..., Y_n) data resulting from a parametric law F of scalar parameter θ. A confidence interval (B_i, B_s) is a statistic in the form of an interval containing θ with a specified probability.

  11. final def eq(arg0: AnyRef): Boolean

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  12. def equals(arg0: Any): Boolean

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  13. def finalize(): Unit

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  14. final def getClass(): Class[_]

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  15. def hashCode(): Int

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  16. final def isInstanceOf[T0]: Boolean

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  17. def mean(seq: Seq[Double]): Double

    Computes the mean of the sequence of measurements.

  18. final def ne(arg0: AnyRef): Boolean

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  19. final def notify(): Unit

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  20. final def notifyAll(): Unit

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  21. def stdev(seq: Seq[Double]): Double

    The sample standard sample deviation.

    The sample standard sample deviation. It is the square root of S², unbiased estimator for the variance.

  22. final def synchronized[T0](arg0: ⇒ T0): T0

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  23. implicit def test2boolean(t: Test): Boolean

  24. def toString(): String

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  25. final def wait(): Unit

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  26. final def wait(arg0: Long, arg1: Int): Unit

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  27. final def wait(arg0: Long): Unit

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