Multivariate Statistics is the study of observing multiple variables simultaneously and analyzing the effect of each individual variable in a statistics model, how each variable relates to each other, and the total effect of the combined variables. Multivariate Statistics provides the means to distinguish the individual effects of variables on the entire model and the individual significance of each independent variable. Important topics include contingency table analysis, linear discriminant analysis, regression analysis, and multivariate analysis.

We provide comprehensive Multivariate Statistics tutoring for students including the following Multivariate Statistics topics:

• Bonferroni Method
• Canonical Correlation Analysis
• Canonical Variates
• Cluster Analysis
• Clustering Systems
• Cochran Theorem
• Component Analysis
• Confirmatory Factor Analysis
• Contingency Table Analysis
• Correlated Predictors
• Discriminant Analysis
• Factor Analysis
• Factor Models
• Factor Solutions
• Fisher’s Approach
• Inverse Design
• K-Group Problem
• Latent Class Modeling
• Latent Structure Analysis
• Likelihood Function
• Linear Discriminant Analysis
• Linear Structural Relation
• Logistic Discrimination
• Logit Models
• Loglinear Models
• Mancova
• Manova
• Measure of Fit
• Moments of Multidimensional Variables
• Monte Carlo Simulation
• Multicollinearity
• Multidimensional Effect Patterns
• Multidimensional Scaling
• Multidimensionality
• Multinomial Distribution
• Multiple Cause Models
• Multiple Coefficient of Determination
• Multiple Discriminant
• Multivariate Analysis
• Multivariate Normal Distribution
• Ordinary Least Squares Approach
• Partial F Values
• Partial Least Squares
• Q Type Analysis
• R software
• Recursive Portioning
• Redundancy Analysis
• Regression Analysis
• RV coefficient
• SAS software
• SPSS software
• Stata software
• Strucutural Equation Model
• Two Group Discriminant Analysis
• Wishart Distribution