**Degrees of freedom (statistics) - Wikipedia**
https://en.wikipedia.org/wiki/Degrees_of_freedom_(statistics)

Geometrically, the degrees of freedom can be interpreted as the dimension of certain vector subspaces. As a starting point, suppose that we have a sample of independent normally distributed observations, $${\displaystyle X_{1},\dots ,X_{n}.\,}$$This can be represented as an n-dimensional random vector: $${\displaystyle {\begin{pmatrix}X_{1}\\\vdots \\X_{n}\end{pmatrix}}.}$$Since this random vector can lie anywhere in n-dimensional space, it has n degrees of freedom.

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