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 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.
Examples of multivariate analysis in action
For decades process and product quality issues have been solved with our industry leading multivariate analysis tool, Unscrambler.
Using multivariate analysis Nidar is able to resolve product quality problems and optimise their manufacturing processes from better process understanding.
Multivariate analysis helps SAB Miller create leading brands and get greater value from theirmanufacturing and market research data.
HarvestLab® from ZEISS and John Deere uses multivariate analysis for on-the-go measuring of moisture, dry matter, protein, starch, fiber, neutral detergent fiber, acid detergent fiber, and sugar.>/h3>
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