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Welch's t-test: Difference between revisions
Scala solution added
m (not wise to put fist a solution which is buggy, not written following common coding conv, and more complicated than necessary (numy is actually used, especially for stats using pandas) |
(Scala solution added) |
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<pre>-9.559498 2.0008523 0.0107516</pre>
=={{header|
<lang Scala>import org.apache.commons.math3.distribution.TDistribution
object WelchTTest extends App {
val res = welchTtest(Array(3.0, 4.0, 1.0, 2.1), Array(490.2, 340.0, 433.9))
def welchTtest(x: Array[Double], y: Array[Double]) = {
def square[T](x: T)(implicit num: Numeric[T]): T = {
import num._
x * x
}
def count[A](a: Seq[A])(implicit num: Fractional[A]): A =
a.foldLeft(num.zero) { case (cnt, _) => num.plus(cnt, num.one) }
def mean[A](a: Seq[A])(implicit num: Fractional[A]): A = num.div(a.sum, count(a))
def variance[A](a: Seq[A])(implicit num: Fractional[A]) =
num.div(a.map(xs => square(num.minus(xs, mean(a)))).sum, num.minus(count(a), num.one))
val (nx, ny) = (x.length, y.length)
val (vx, vy) = (variance(x), variance(y))
val qt = vx / nx + vy / ny
val t = (mean(x) - mean(y)) / math.sqrt(qt)
val df = square(qt) / (square(vx) / (square(nx) * (nx - 1)) + square(vy) / (square(ny) * (ny - 1)))
val p = 2.0 * new TDistribution(df).cumulativeProbability(-math.abs(t))
(t, df, p)
}
println(s"t = ${res._1}\ndf = ${res._2}\np = ${res._3}")
println(s"\nSuccessfully completed without errors. [total ${scala.compat.Platform.currentTime - executionStart} ms]")
}</lang>=={{header|Scilab}}==
{{trans|Stata}}
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