DiscretePlot[Table[PDF[HypergeometricDistribution[n,50,100],k],{n,{10}}]//Evaluate,{k,0,32},PlotRange->All]

PDF[HypergeometricDistribution[n,r,R],x]

Let’s see the result when X and Y both follow Normal Distribution (just for fun)

(*Define the random variables*)
X=NormalDistribution[0,1];
Y=NormalDistribution[0,1];

Z=TransformedDistribution[x+y,{x\[Distributed]X,y\[Distributed]Y}];
(*Display the distribution of Z*)
Z
V=TransformedDistribution[x*y,{x\[Distributed]X,y\[Distributed]Y}];
V
W=TransformedDistribution[y/x,{x\[Distributed]X,y\[Distributed]Y}];
W

Plot[Table[PDF[ChiDistribution[ν],x],{ν,{.5,1,4}}]//Evaluate,{x,0,4},Filling->Axis]

PDF[ChiDistribution[ν],x]

Plot[Table[PDF[ChiSquareDistribution[ν],x],{ν,{0.5,3,5}}]//Evaluate,{x,0,6},Filling->Axis]

PDF[ChiSquareDistribution[ν],x]

X=ChiSquareDistribution[m];
Y=ChiSquareDistribution[n];
Z=TransformedDistribution[x+y,{x\[Distributed]X,y\[Distributed]Y}];
Z

Plot[Table[PDF[WeibullDistribution[α,2],x],{α,{0.5,2,4}}]//Evaluate,{x,0,5},Filling->Axis]

PDF[WeibullDistribution[α,β,μ],x]

PDF[WeibullDistribution[α,β],x]

PDF[WeibullDistribution[β,1/α],x]