Multivariate Analysis Methods and Their Use
Multivariate analysis of data is basically a technique of statistics which is used to interpret the data that comes from more than a variable. Most importantly, multivariate data analysis gives an overview of the reality in which every product, situation as well as decision includes above one variable. The age of information has brought in a lot of data in each field. Irrespective of the masses of data present, the capability to get a clear idea of what is happening and to make appropriate decision is tough. When the information present is gathered in the database tables having columns and rows, multivariate data analysis can help in processing the information into meaningful ways.
What are the Uses of Multivariate Analysis Methods?
Following are the main uses of multivariate analysis methods:
- Market and consumer research
- Assurance of quality and quality control in several fields like pharmaceuticals, food and beverage, energy, chemicals, paint, telecom and many more
- Research and development
- Process control along with process optimization
Benefits of Multivariate Analysis
There are a range of different benefits associated with multivariate analysis:
Getting an Overview of Table or a Summary
This kind of interpretation is sometimes known as the factor analysis of the principal component analysis. In the case of the overview, there is a possibility to determine the patterns which are dominant in data like the trends, groups as well as outliers. These patterns are shown in the form of two different plots.
Disrciminant or Classification Analysis
Here the groups in the table are analyzed in terms of how they are different from one another and to which table rows the group individual belongs. This kind of analysis is known as the Disrciminant or Classification Analysis.
Determining Links between Columns of a Data Table
The links between the columns of the data table need to be determined. For example it can include finding the link between the product quality as well as the process operation conditions. The aim of this is to make use of a single variable set like the columns in order to predict the other one due to optimization and to determine which columns are essential in the linking. Such a corresponding analysis is known as the partial least square or the multiple regression analysis on the basis of the data table’s size.