## First approach ## You might try this approach in Mathematica. Let's generate some bivariate data: data = Table[RandomVariate[BinormalDistribution[{50, 50}, {5, 10}, .8]], {1000}]; Then we need to load this package: Needs["MultivariateStatistics`"] And, now: EllipsoidQuantile[data, {0.9}] gives an output that defines a 90% confidence ellipse. The values you obtain from this output are in the following format: {Ellipsoid[{x1, x2}, {r1, r2}, {{d1, d2}, {d3, d4}}]} x1 and x2 specify the point at which the ellipse in centered, r1 and r2 specify the semi-axis radii, and d1, d2, d3 and d4 specify the alignment direction. You can also plot this: Show[{ListPlot[data, PlotRange -> {{0, 100}, {0, 100}}, AspectRatio -> 1], Graphics[EllipsoidQuantile[data, 0.9]]}] ## Second approach ## This approach is based on the smooth kernel distribution. These are some data distributed in a similar way to your data: data1 = RandomVariate[BinormalDistribution[{.3, .7}, {.2, .3}, .8], 500]; data2 = RandomVariate[BinormalDistribution[{.6, .3}, {.4, .15}, .8], 500]; data = Partition[Flatten[Join[{data1, data2}]], 2]; We obtain a smooth kernel distribution on these data values: skd = SmoothKernelDistribution[data]; We obtain a numeric result for each data point: eval = Table[{data[[i]], PDF[skd, data[[i]]]}, {i, Length[data]}]; We fix a threshold and we select all the data that are higher than this threshold: threshold = 1.2; dataIn = Select[eval, #1[[2]] > threshold &][[All, 1]]; Here we get the data that fall outside the region: dataOut = Complement[data, dataIn]; And now we can plot all the data: Show[ContourPlot[Evaluate@PDF[skd, {x, y}], {x, 0, 1}, {y, 0, 1}, PlotRange -> {{0, 1}, {0, 1}}, PlotPoints -> 50], ListPlot[dataIn, PlotStyle -> Darker[Green]], ListPlot[dataOut, PlotStyle -> Red]] The green colored points are those above the threshold and the red colored points are those below the threshold. ![enter image description here][1] [1]: https://i.sstatic.net/ITM29.jpg