data1={{2014.,0.015},{2015.,0.005},{2016.,0.0},{2017.,0.01},{2018.,0.02},{2019.,0.014}};

ListPlot[data1
  ,Frame->True
  ,PlotRange->{{2013.,2022.},{-0.01,0.03}}
  ,PlotStyle->Directive[Orange,PointSize[Large]]
]

Mean[data1]
StandardDeviation[data1]

data2=Standardize[data1];

ListPlot[data2
  ,Frame->True
  ,PlotRange->{{-2.,2.},{-2.,2.}}
  ,PlotStyle->Directive[Orange,PointSize[Large]]
]

lmFit[data_List,degree_Integer]:=LinearModelFit[data,Table[x^i,{i,degree}],x]

lmFitPlot[data_List,degree_Integer,{xmin_,xmax_,ymin_,ymax_}]:=Module[{lmf,ss},

  lmf=lmFit[data,degree];
  ss=Total[lmf["FitResiduals"]^2]; (* Sum of squared residuals *)

  Show[
    {Plot[lmf[x],{x,xmin,xmax}
     ,PlotRange->{{xmin,xmax},{ymin,ymax}}]
    ,ListPlot[data,PlotStyle->Directive[Orange,PointSize[Large]]]
    }
    ,Frame->True
    ,FrameLabel->{{"",""},{"Year",Row[{"Sum squared residuals= ",ss}]}}
    ,ImageSize->Medium
  ]
]

lmFitPlot[data1,5,{2013.,2022.,-0.01,0.03}]

lmFitPlot[data2,5,{-2.,2.,-2.,2.}]

MatrixRank[lmFit[data1,5]["DesignMatrix"]]
MatrixRank[lmFit[data2,5]["DesignMatrix"]]

FindGeometricTransform[data1,data2]