Linear Regression in Scala

LinearRegression.scala

object LinearRegression { 

  def fit(data: List[(Double,Double)]) = {
    def sqr(in:Double) : Double = in * in
    val sumx = data.map(_._1).sum
    val sumx2 = data.map((d:(Double,Double)) => sqr(d._1)).sum
    val sumy = data.map(_._2).sum
    val xbar = sumx / data.length
    val ybar = sumy / data.length
    val xxbar = data.map(d => sqr(d._1 - xbar)).sum
    val yybar = data.map(d => sqr(d._2 - ybar)).sum
    val xybar = data.map(d => (d._1 - xbar) * (d._2 - ybar)).sum
    val m = xybar / xxbar
    val c = ybar - (m * xbar)
    val out = for(d <- data) yield {
      val fit = m * d._1 + c
      (sqr(fit - d._2), sqr(fit - ybar))
    }
    val rss = out.map(_._1).sum
    val ssr = out.map(_._2).sum
    val rsquared = ssr / yybar
    val svar = rss / (data.length - 2).toDouble
    val stErrGradient = svar / xxbar
    val stErrIntercept = (svar / data.length) + (sqr(xbar) * stErrGradient)
    println("y   = " + m.toString + " x + " + c.toString)
    println("R^2 Correlation Coefficient = " + rsquared.toString);
    println("Std. error of m = " + math.sqrt(stErrGradient).toString);
    println("Std. error of c = " + math.sqrt(stErrIntercept).toString);
  }

}

m = gradient
c = y-axis intercept