A person who wants to earn a degree in data science, data analytics, mathematics, statistics or a related field of expertise might ask, “What is multivariate analysis?” This is a statistical concept related to the study of complex sets of data. Anyone who plans a career in a field that will require the use of large data sets should understand what multivariate analysis is, how it works, its methodology and how it is typically accomplished.

### What is Multivariate Analysis?

Multivariate analysis is a process of analyzing a complicated and large set of data. It is able to observe and analyze more than one statistical outcome at a time. It is the statistical method of choice when there are multiple measurements on each experimental unit in the data set. An experimental unit could be blood pressure, a person’s weight or the price of a stock. The analysis can be set up as a type of hierarchy.

### When Should Multivariate Analysis Be Used?

Multivariate analysis is often used in order to reduce the likelihood of Type I errors. A Type I error is an incorrect rejection of a null hypothesis. For example, a null hypothesis might be that Drug A will have no effect on blood pressure. Multivariate analysis should be used when a univariate or single variable analysis would not thoroughly answer the problem at hand or address the situation of the data. It should also be used when a person wants to know more about the structure of the data itself.

### Types of Multivariate Analysis

There are more than 20 ways to perform multivariate analysis, explains Data Central’s Statistics How-to. The one that a data analyst picks will depend on the type of data they have and what the goal of the project is. Some data sets will only be suitable for one type of multivariate analysis. However, most data sets will be compatible with several different methods of multivariate analysis. Some of the types of multivariate analysis include additive trees, cluster analysis, redundancy analysis, factor analysis, multidimensional scaling and multiple regression analysis. Some of the methods also include independent component analysis, principal component analysis, and partial least square regression.

### Tools Used for Multivariate Analysis

Because of the complexity of multivariate analysis, it is not practical to try to do it by hand. A calculator would also be inadequate as a tool for multivariate analysis. Most data analysts, statisticians and other professionals who would need to conduct a multivariate analysis on a set of data would use a statistical software program for the project. This involves writing code that the computer software understands. The code requires knowledge of functions and syntax. A few of the statistical analysis software programs used for multivariate analysis include SPSS, SAS and STATA.

### Conclusion

A person who understands what multivariate analysis is will have the knowledge needed in order to determine how to go about analyzing a complex set of data. There are a lot of ways to perform a multivariate analysis, and data analytics, statistics or related professionals will have to understand their differences and when to use which one in order to make sense of the data that they collect. Being able to answer, “What is multivariate analysis?” sets up a person for success in their future career in data analytics.

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