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Multivariate Analysis In Machine Learning

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Multivariate Analysis

Multivariate analysis is a set of statistical techniques used for analysis of data that contain more than one variable.

Multivariate analysis is used widely in many industries, like healthcare. In the recent event of COVID-19, a team of data scientists predicted that Delhi would have more than 5lakh COVID-19 patients by the end of July 2020. This analysis was based on multiple variables like government decision, public behavior, population, occupation, public transport, healthcare services, and overall immunity of the community.  

Advantages Of Multivariate Analysis


  • The main advantage of multivariate analysis is that since it considers more than one factor of independent variables that influence the variability of dependent variables, the conclusion drawn is more accurate.

  • The conclusions are more realistic and nearer to the real-life situation.

Multivariate Analysis Techniques

  • Canonical Correlation Analysis

  • Structural Equation Modelling

  • Interdependence Technique

  • Factor Analysis 

  • Cluster analysis

  • Multidimensional Scaling

  • Correspondence analysis 

The Objective of multivariate analysis

  • Data reduction or structural simplification

  • Sorting and grouping

  • Investigation of dependence among variables

  • Prediction Relationships between variables

  • Hypothesis construction and testing

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