Analyzing and Understanding the Impact of Multivariate Relationships  

Application Area:
Statistics, Predictive Modeling and Data Mining

See how to detect and deal with correlated variables in your analyses to overcome issues that can arise when the degree of variable correlation is high enough that it can cause problems when you fit the model and interpret the results. Learn to handle collinearity between predictor variables and model the variation in a set of variables in terms of a smaller number of independent linear combinations (principal components).

This webinar covers: Multivariate models, collinearity, Variance Inflation Factor (VIF), Principal Component Analysis(PCA), Scatterplot Matrix, Correlation Probability, Outlier Relationships