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Under the unrestricted model, insert the maximum-likelihood estimates $(\bar{x},S)$. Under the diagonal-covariance restriction, the quadratic form is minimized over the mean at $\bar{x}$, and the remaining objective separates over the diagonal entries:Differentiating in $v_j>0$ gives ${\hat{v}}_j=S_{jj}$; the derivative changes from negative to positive there. Put $D=\mathrm{diag}(S_{11},\dots ,S_{pp})$. The fitted trace term is $p$ under either model, since $\mathrm{tr}(D^{-1}S)=p=\mathrm{tr}(S^{-1}S)$. Consequently the likelihood-ratio test statistic is determined byThis is the Gaussian diagonal-covariance likelihood-ratio test. Reject for sufficiently small $\log |R|$, equivalently for large $-n\log |R|$. Under the regular large-sample calibration, Wilks theorem gives $-n\log |R|\Rightarrow {\chi }_{p(p-1)/2}^2$, since diagonality removes that many free covariance parameters. The exact critical constant can instead be obtained from the null distribution at the actual sample size.

For $p=2$, write the sample correlation coefficient as $r$. Then $|R|=1-r^2$, so the likelihood-ratio test rejects for large $|r|$, not merely for large positive $r$. Under the null, the two variables have a bivariate normal distribution and are independent, and the exact test isIndeed, condition on the centered first-variable observation vector. Decompose the second centered Gaussian vector into its component along that direction and the $n-2$-dimensional orthogonal complement. Their squared lengths give independent ${\chi }_1^2$ and ${\chi }_{n-2}^2$ variables. Their ratio shows that $r\sqrt{(n-2)/(1-r^2)}$ has Student's t-distribution with $n-2$ statistical degrees of freedom.

Calculate the observed sample correlation coefficient $\hat{r}$. In each paired bootstrap, draw 100 indices independently with replacement from $1,\dots ,100$ and select the corresponding whole pairs. Compute the bootstrap correlation $r_b^{\ast }$ from each resample; do not resample the two margins independently, since that would destroy their empirical dependence.

Let $q_p$ be the empirical $p$-quantile of those bootstrap correlations. The percentile bootstrap confidence interval isThis is the requested algorithmic interval; the actual endpoints require the observed pairs. Its coverage is approximate, based on the sampling law of the correlation being well approximated by the bootstrap law. Both sample variances must be positive. Resamples having a constant margin have undefined correlation and cannot be repaired by arbitrarily assigning correlation zero. One may regenerate these exceptional resamples, explicitly conditioning on well-defined correlations; under the usual nondegenerate large-sample conditions their probability is negligible. Frequent degeneracy instead indicates that this ordinary correlation-bootstrap construction is unsuitable.

For paired observations $(x_i,y_i)$, the sample correlation coefficient isIt is the normalized inner product of the centered data vectors, or the sample covariance divided by the product of the corresponding sample standard deviations. The choice of divisor $n$ or $n-1$ cancels when used consistently in all three quantities. It is undefined if either centered data vector is zero, which occurs when that variable is constant in the observed statistical sample.

For $x=(1,2,3,4)$ and $y=(2,1,4,3)$, both sample means are $2.5$. The sum of centered products is $3$, and each sum of centered squares is $5$, hence the sample correlation coefficient is $3/5=0.6$. This is a summary of these four observed pairs; it is not automatically the population Pearson correlation coefficient. Philip Stark, Computing the correlation coefficient explains the centered and standardized calculation.

A sample correlation matrix rescales a sample covariance matrix $S$ to unit diagonal, for variables with positive sample variance. Its off-diagonal entries are sample correlation coefficients. Strong correlations between predictors can indicate multicollinearity, but pairwise correlations alone do not exclude higher-order linear dependence.