Statistical significance in canonical correlation analysis I do canonical correlation analysis between two multivariate datasets $X$ and $Y$. For each pair of canonical variates (x-y pair) I get the canonical correlation coefficient. How can I test its statistical significance?
 A: Let $p_x$ and $p_y$ be the number of variables in your sets $X$ and $Y$. $N$ is the sample size. You have obtained $m=\min(p_x,p_y)$ canonical correlations $\gamma_1, \gamma_2,...,\gamma_m$. Testing them usually goes as follows.
Given $\gamma_j$, its corresponding eigenvalue is $\lambda_j= \frac{1}{1-\gamma_j^2}-1$.
Wilk's lambda statistic for it is $w_j= \frac{1}{1+\lambda_j}w_{j+1}$. So, first compute $w_m$ which is $\frac{1}{1+\lambda_m}$, then compute $w_{m-1}$ using $w_m$, etc., backwards.
This statistic has approximately Chi-square distribution (under assumptions of normality and large $N$) with $df_j= (p_x-j+1)(p_y-j+1)$. To recalculate Wilk's into the Chi-square: $\chi_j^2= -\ln(w_j)(N-(p_x+p_y+3)/2)$.
So, substitute $\chi_j^2$ in Chi-square cdf distribution with $df_j$, subtract from 1, and have the p-value for correlation $\gamma_j$.
What does this p-value mean in fact? Nonsignificant p-value for $\gamma_1$ tells that all canonical correlations $\gamma_1$ through $\gamma_m$ are not significant (i.e. the hypothesis that they all are zero should not be rejected). Significant p-value for $\gamma_1$ and nonsignificant p-value for $\gamma_2$ tells that $\gamma_1$ is significant (likely to be nonzero in the population), while the rest $\gamma_2$ through $\gamma_m$ are all not significant; etc. Sometimes, p-value for $\gamma_{j+1}$ is lower than for $\gamma_{j}$. That should not be taken in the sense "$\gamma_{j+1}$ is more significant" because a more junior correlation cannot be more significant than more senior one. As said already, if $\gamma_{j}$ is not significant for you, all the remaining junior correlations must automatically be considered not significant too.
For an algorithm of CCA, look here.
