Singular Values & the SVD · Hard problem

Least-squares problems are sometimes solved through the normal equations $A^{T}Ax = A^{T}b$. For $A = \begin{pmatrix}1 & 1\\1 & -1\\1 & 2\end{pmatrix}$, find the condition number $\kappa(A^{T}A) = \dfrac{\sigma_{\max}(A^{T}A)}{\sigma_{\min}(A^{T}A)}$.