Generally, you use the `sdo.scatterPlot(X,Y)` syntax
with `X` specifying the samples and `Y` specifying
the cost function value for each sample. Use the `sdo.evaluate` command
to perform the cost function evaluation to generate `Y`.
For this example, obtain 100 samples of the `Ac` and `K` parameters
of the `sdoHydraulicCyclinder` model. Calculate the
cost function as a function of `Ac` and `K`.
Create a scatter plot to see the sample and cost function values.

Load the `sdoHydraulicCyclinder` model.

load_system('sdoHydraulicCylinder');

Generate 100 samples of the `Ac` and `K` parameters.

p = sdo.getParameterFromModel('sdoHydraulicCylinder',{'Ac','K'});
ps = sdo.ParameterSpace(p);
X = sdo.sample(ps,100);

The first operation obtains the `Ac` and `K` parameters
as a vector, `p`. The second operation creates an `sdo.ParameterSpace` object, `ps`,
that specifies the probability distributions of the parameter samples.
The third operation generates 100 samples of each parameter, returned
as a `Table`, `X`.

Calculate the cost function value table.

Ac_mean = mean(X{:,1});
K_mean = mean(X{:,2});
Y = table(X{:,1}/Ac_mean+X{:,2}/K_mean,'VariableNames',{'y'});

Create a scatter plot of `X` and `Y`.

sdo.scatterPlot(X,Y);