## Multivariate analysis

The idea of multivariate analysis was first proposed in 1960 by Anderson in his book, an introduction to multivariate statistical analysis. However, by that time, the realm of data analysis was only a theoretical one. Statisticians at that time knew that a time would come where they would process a large amount of data, and the data itself could be readily available. That is the world where we currently live today. With the advent of computers, a large amount of data analysis is possible even at an individual level. You can now conduct a multivariate analysis on your personal computer using Stata.

### What is multivariate analysis?

Let's take the example that you want to purchase a car. How many variables would you consider before coming to a conclusion about which car to buy? Of course, you will consider the price, fuel consumption, and the cost of repairing the car. There are a lot of variables that you have to take into account before you buy a car.

The problem described here, requires a statistical analysis that will incorporate all the variables. That is what multivariate analysis does. Multivariate analysis, as the name suggests, is a form of analysis that incorporates a lot of variables. Simply put, multivariate analysis is a data analysis method where we analyze more than one variable. Most of the multivariate analysis is very cumbersome if done manually, especially those that involve a lot of variables. Therefore, most of the analysis is done using software that can analyze faster, reduce errors, and give more accurate results.

Multivariate analysis has lots of applications in our daily life. It's an arsenal to a marketer who wants to convince more people to buy a product. In addition, the everyday decisions that we make which incorporate a lot of variables is a case of multivariate analysis.

### Multivariate analysis approach

To conduct a successful multivariate analysis, there are two key factors that should always be considered first.

**1. The problem.**

First, we must understand what the problem is. This way, we can collect the relevant data needed for the analysis. Note that, out there, there is an abundance of data. To conduct a robust analysis, you must use the appropriate data.

**2. The appropriate analysis tool.**

The tool that you are using for analysis also has a significant effect on the result. By the term "tool," we imply a statistical analysis method. Multivariate analysis is a general term for statistical analysis methods that involve a lot of variables. The multivariate analysis statistical methods that are commonly used are Principal component analysis(PCA), Factor, cluster, multiple regression, discriminant, and classification analysis

### What are the advantages of multivariate analysis?

The main advantage of multivariate analysis is the fact that in their analysis, we use a lot of variables. As such, they are able to answer more complex questions than univariate analysis methods. If we were using a univariate method with the same analysis, we would have to conduct the analysis over and over changing the variables so that we can come up with the solution.

#### Limitations

Multivariate analysis is cumbersome. To conduct an effective analysis, we have to use statistical software, which is worsened further by the fact that this statistical software is expensive, and most people cannot afford it. In terms of data, most multivariate analyses require a lot of data. Perhaps, this could be the reason why we have to use statistical software for the analysis.

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