anchor.diff:      
   let fittingLS = 
       let fit = 
  -        [|0. .. 4.|] 
  +        [|0. .. 11.|] 
           |> Array.map (fun x -> x,fittingFunctionLinearLS x)
       Chart.Line(fit)
  -    |> Chart.withTraceName "least squares"
  +    |> Chart.withTraceName "least squares (LS)"
   
   let fittingRobust = 
       let fit = 
  -        [|0. .. 4.|] 
  +        [|0. .. 11.|] 
           |> Array.map (fun x -> x,fittingFunctionLinearRobust x)
       Chart.Line(fit)
       |> Chart.withTraceName "TheilSen estimator"
   
   let fittingRTO = 
       let fit = 
  -        [|0. .. 4.|] 
  +        [|0. .. 11.|] 
           |> Array.map (fun x -> x,fittingFunctionLinearRTO x)
       Chart.Line(fit)
  -    |> Chart.withTraceName "through origin"
  -
  -(*** do-not-eval***)
  -[rawChart;fittingLS;fittingRobust;fittingRTO] 
  -|> Chart.Combine
  -|> Chart.Show
  -
  +    |> Chart.withTraceName "LS through origin"
  +
  +let simpleLinearChart =
  +    [rawChart;fittingLS;fittingRTO;fittingRobust;] 
  +    |> Chart.Combine
  +
  +(*** include-value:simpleLinearChart ***)
   
   //Multivariate simple linear regression
   let xVectorMulti =
  
positive.code: open FSharp.Plotly
  
  
  
  
  (**
  
  #Fitting
  ##Linear Regression
  
  In Linear Regression a linear system of equations is generated. The coefficients obtained by the solution to this equation 
  system minimizes the distance from the regression curve to the data points. These distances are also known as residuals (or least squares).
  
  ###Simple Linear Regression
  
  Simple linear regression aims to fit a straight regression line to the data
  
  While the least squares approach efficiently minimizes the sum of squared residuals it is prone to outliers. 
  An alternative is a robust simple linear regression like Theil's incomplete method or the Theil-Sen estimator, that are outlier resistant.
  
  *)
  
negative.code:     
  let fittingPol = 
      let fit = 
          [|1. .. 0.1 .. 10.|] 
          |> Array.map (fun x -> x,fittingFunctionPol x)
      Chart.Line(fit)
      |> Chart.withTraceName "order = 3"
  
  let fittingPolW = 
      let fit = 
          [|1. .. 0.1 .. 10.|] 
          |> Array.map (fun x -> x,fittingFunctionPolW x)
      Chart.Line(fit)
      |> Chart.withTraceName "order = 3 weigthed"
  
  let polRegressionChart =
      [rawChartP;fittingPol;fittingPolW] 
      |> Chart.Combine
  
  (*** include-value:polRegressionChart ***)
  
  
  (**
  ##Nonlinear Regression
  