Multivariate Data Analysis

Multivariate analysis methods typically used for:

• Consumer and market research
• Quality control and quality assurance across a range of industries such as food and beverage, paint, pharmaceuticals, chemicals, energy, telecommunications, etc
• Process optimization and process control
• Research and development

With Multivariate Analysis you can:

Principal Component Analysis


MVA for Spectral data

• 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
• Analyze 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

Tools for Multivariate Analysis

Among the various, multivariate tools available, The Unscrambler® stands out as an all-in-one multivariate data analysis software product. This product and related ones from CAMO are proven tools that have enabled different organizations solve their Multivariate Analysis requirements.