Recall the globalLandTempHist.txt dataset that consisted of the global land temperature of Earth over the past 300 years. Also recall that a covariance matrix is a symmetric positive-definite square matrix of the form,
\[\Sigma = \begin{pmatrix} \sigma_{11} & \sigma_{12} \\ \sigma_{21} & \sigma_{22} \end{pmatrix}\]where each element is computed via the following equation,
\[\sigma_{ij} = \sum_1^{n} ~ (x_i-\overline{x})(y_j-\overline{y})\]where $n$ represents the number of data points and $x_i$ and $x_j$ are individual data points. Use the covariance matrix definition and the above equation to compute the covariance matrix of the two year and the temperature anomaly attributes in the data referenced above. To do so, first write a generic function genCovMat(Data) that takes an input matrix (table) of data and generates the covariance matrix of data.
