Multivariate Data Analysis Glossary - Sponsored Whitepaper

Most of the problems in the world are multivariate in nature - meaning that there are many variables that contribute to them. We cannot simply use the season of the year to predict the weather, as many other variables are part of the relationship to weather. The same holds true with other scientific, economic, and consumer preference relationships, such as product quality, which is influenced by numerous process variables, or consumer preference for a product which can be based on traits such as color, taste and price.

This short guide gives you an introduction to the principles of multivariate analysis, some broad applications for this technology, how it differs from classical (univariate) statistics and an overview of common multivariate models.

Imagine out of the five senses you only had sight. From your perspective you could see the world but you would not be able to hear the sounds around you, smell, taste or feel things. Your understanding of the world would be more limited.

In the event of danger, provided you were pointed in the direction of the danger, you would have some chance of avoiding it. But if it was behind you, you would never hear the danger, or know that it was coming. With the combination of sight and sound it is easier to avoid danger. Even with two of the five senses, your view of the world is still limited.

Multivariate analysis is a tool to find patterns and relationships between several variables simultaneously.