Multivariate analysis

The world is multivariate.
Your analysis should be as well.

Multivariate analysis is a set of techniques used for analysis of data that contain more than one variable. There is always more than one side to the problem you are trying to solve. It’s the same in your data.

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

The digital economy generates fast growing masses of data from old and new industrial infrastructures. This data holds the potential to be the most valuable asset for your company. Sure, there is value in aggregating and visualising the data, but there is always more than one side to the problem you are trying to solve. It’s the same in your data. The only way to solve the complex problems and realise the full potential is by analysing all variables and dimensions of the data using multivariate analysis. Only then will you get insights that reflects your industrial reality – and how to optimise it.

Multivariate analysis refers to any statistical technique used to analyse more complex sets of data. There are more than 20 different methods to perform multivariate analysis and which method is best depends on the type of data and the problem you are trying to solve. Essentially you build models that reflects an actual product or process and optimise it using different methods.

Multivariate analysis is typically used for:

  • Quality control and quality assurance
  • Process optimisation and process control
  • Research and development
  • Consumer and market research

How multivariate methods are used

  • Obtain a summary or an overview of a table. This analysis is often called Principal Components Analysis or Factor Analysis. In the overview, it is possible to identify the dominant patterns in the data, such as groups, outliers, trends, and so on. The patterns are displayed as two plots
  • Analyse groups in the table, how these groups differ, and to which group individual table rows belong. This type of analysis is called Classification and Discriminant Analysis
  • Find relationships between columns in data tables, for instance relationships between process operation conditions and product quality. The objective is to use one set of variables (columns) to predict another, for the purpose of optimization, and to find out which columns are important in the relationship. The corresponding analysis is called Multiple Regression Analysis or Partial Least Squares (PLS), depending on the size of the data table

The multivariate difference

Multivariate analysis provides a more accurate view of the behavior between variables that are highly correlated, and can detect potential problems in a product or process.

Follow the red-dots: Looking at the variables individually there are no apparent issue but combining them in a multivariate view immediately reveals an issue

Many decisions are based on univariate analysis, but only multivariate analysis reveals relationships that help you detect problems that are not obvious by looking at the variables individually.

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