anchor.diff:                          ) (0.,0.)
           ((SST - SSE) / SST)
    
  +    /// Gets the adjusted coefficient of determination, as known as the R-Squared (R²adj). It is adjusted by the number of used variables (not including the constant term) (https://ebrary.net/1008/economics/adjusted_coefficient_determination_adjusted)
  +    let calculateDeterminationAdj (actual:seq<float>) (expected:seq<float>) (variables:int) =
  +        let dataLength = Seq.length actual |> float
  +        let tmpAdj = (1. - calculateDeterminationFromValue actual expected) * (dataLength - 1.) / (dataLength - 1. - (float variables))
  +        1. - tmpAdj
   
       /// Calculates Akaike information criterion (AIC) which is a measure of the relative quality of a regression model for a given set of data    
       // ! Formula used for regression only (because number of model parameter are missing)
  
positive.code:         ///    regression approximates the real data points. An R^2 of 1.0 indicates that the
          ///    regression perfectly fits the data.
      *)
      let calulcateDeterminationFromValue (actual:seq<float>) (expected:seq<float>) = 
          let meanY = Seq.mean actual
          let SSE,SST =
              Seq.zip actual expected
  
negative.code:         let TTest = Testing.TestStatistics.createTTest statistic (sumOfSquares.Count - 2.)
          TTest
  
      let calculateDetermination (sumOfSquares:SumOfSquares) =
          sumOfSquares.Regression / sumOfSquares.Total
          
  
  