Canonical Correlation Analysis (CCA). CCA. This is it! The mother of all linear statistical analysis. When ? We want to find a structural relation between a set of independent variables and a set of dependent variables. CCA. When ? (part 2)
Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.
Since all canonical variables are significant, we will keep them all.
Correlation matrix of the dependant variables
The first canonical variate from IVs extract 40% of the variance in the y variable.
The second canonical variate form IVs extract 30% of the variance in the y variable.
Together they extract 70% of the variance in the DVs.
The first canonical variate from DVs extract 49% of the variance in the x variable.
The second canonical variate form DVs extract 24% of the variance in the x variable.
Together they extract 73% of the variance in the IVs.
= Loading matrix =