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Learn what the covariance matrix is, how to calculate it, and how to use it for reducing the number of variables or features in a multivariate normal distribution.
Learn how to present and explain latent class analysis findings in a clear and engaging way with tips and examples.
Learn how to perform multidimensional scaling (MDS) in R with categorical and mixed data types, and how to visualize and interpret the results.
Learn how to monitor, evaluate, and maintain your cluster model for data segmentation using cluster analysis. Discover how to use your cluster model as a source of…
Learn how to use fit indices and hypothesis testing to compare and test different SEM models based on data and theory. Find out how to select, validate, and report…
Learn about Bayesian SEM, multilevel SEM, SEM with categorical outcomes, SEM with machine learning, and SEM with big data in this article.
Learn some best practices or tips for reporting and presenting Hotelling's T-squared test results, a multivariate technique that compares group means.
Learn how Mahalanobis distance measures multivariate distance and compares with Euclidean, Manhattan, and Cosine distance.
Learn about the differences, advantages, and disadvantages of using PLS-SEM and CB-SEM for multivariate analysis of complex models.
Learn how to perform post-hoc tests and pairwise comparisons after Wilks' lambda in R. Discover different types of follow-up analyses for multivariate data.
Learn how to update and validate PCA models for multivariate outlier detection over time using practical steps and tips on performance, quality, and improvement.
Learn how to optimize the computational efficiency and speed of multidimensional scaling (MDS) in R using some tips and tricks for choosing the type of MDS, using…