= Solution
Take logarithms entry by entry before centering. For tree $i$, let $z_i=(\log G_i,\log H_i,\log V_i)^T$, and put $\bar z=31^{-1}\sum_i z_i$. The command `var(log(b))` computes the <sample covariance matrix> with the <unbiased estimator> normalization
$$
\boxed{\widehat\Sigma=\frac1{30}\sum_{i=1}^{31}(z_i-\bar z)(z_i-\bar z)^T.}
$$
Thus entry $(j,k)$ is the sum of products of the centered log measurements divided by $30$, not the logarithm of the covariance between the original measurements. Its diagonal entries are the three log-variable <sample variances>, and the off-diagonal entries are their <sample covariances>. The positive off-diagonal entries indicate positive association of the log measurements. Taking logarithms turns a multiplicative relation such as $V\approx cG^2H$ into the approximately linear relation $\log V\approx\log c+2\log G+\log H$, which motivates investigating <principal components> of these transformed observations.
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