anchor.diff:  
   // 2.5 create the solverOptions
   let solverOptions = 
  -    let guess = initialParamGuess x_Data y_Data
  +    let guess = initialParamGuess x_DataN y_DataN
       NonLinearRegression.createSolverOption 0.0001 0.0001 1000 guess
   
   
   // 3. get coefficients
  -let coefficientsExp = GaussNewton.estimatedParams model solverOptions x_Data y_Data
  +let coefficientsExp = GaussNewton.estimatedParams model solverOptions x_DataN y_DataN
   //val coefficients = vector [|5.68867298; 0.7263428835|]
   
   
  
positive.code: An alternative is a robust simple linear regression like Theil's incomplete method or the Theil-Sen estimator, that are outlier resistant.
  
  *)
  (*** do-not-eval***)
  open FSharp.Stats
  open FSharp.Stats.Fitting.LinearRegression
  open FSharp.Plotly
  
  let x_Data = vector [|1.;2.;3.;|]
  let y_Data = vector [|4.;7.;9.;|]
  
  //Least squares simple linear regression
  let coefficientsLinearLS = OrdinaryLeastSquares.Linear.Univariable.coefficient x_Data y_Data
  let fittingFunctionLinearLS x = OrdinaryLeastSquares.Linear.Univariable.fit coefficientsLinearLS x
  
  //Robust simple linear regression
  let coefficientsLinearRobust = RobustRegression.Linear.theilSenEstimator x_Data y_Data 
  let fittingFunctionLinearRobust x = RobustRegression.Linear.fit coefficientsLinearRobust x
  
  //least squares simple linear regression through the origin
  let coefficientsLinearRTO = OrdinaryLeastSquares.Linear.RTO.coefficientOfVector x_Data y_Data 
  let fittingFunctionLinearRobust x = OrdinaryLeastSquares.Linear.RTO.fit coefficientsLinearRTO x
  
  
  let rawChart = 
  
negative.code:     xVectorMulti
      |> transformX
      |> vector
  
  let coefficientsMV = 
      OrdinaryLeastSquares.Linear.Multivariable.coefficients xVectorMulti yVectorMulti
  let fittingFunctionMV x = 
      OrdinaryLeastSquares.Linear.Multivariable.fit coefficientsMV x
  
  
  (**
  