Multivariate Data Analysis

The statistical technique used to analyze data that arises from more than one variable. This essentially models reality where each situation, product, or decision involves more than a single variable. The information age has resulted in masses of data in every field. Despite the quantum of data available, the ability to obtain a clear picture of what is going on and make intelligent decisions is a challenge. When available information is stored in database tables containing rows and columns, multivariate analysis can be used to process the information in a meaningful fashion.

The main techniques of multivariate data analysis:

  • Principal Component Analysis (PCA);
  • Confirmatory Factor Analysis (CFA);
  • Factor Analysis;
  • Cluster Analysis;
  • Discriminant Function Analysis;
  • Correspondence Analysis;
  • Conjoint Analysis;
  • Multi-Dimensional Scaling (MDS);
  • Regression Analysis;
  • Logistic Regression Analysis;
  • Path Analysis;
  • Structural Equation Modeling (SEM);
  • Partial Least Square (PLS);
  • Analysis of Variance (ANOVA);
  • Analysis of Covariance (ANCOVA);
  • Multivariate Analysis of Variance (MANOVA);
  • Multivariate Analysis of Covariance (MANCOVA);
  • Canonical Correlation Analysis;
  • etc.

• January 16, 2015

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