Multivariate Data Analysis – An Introduction

“What gets measured, gets managed.” – Peter Drucker

Data and analysis are terms that coexist each depending on the other, that was putting it in simple terms but research and data analysis are much broader terms involving various tools and techniques used to predict the outcome of certain tasks for benefit of an organisation.

One of those analytical techniques used to read large sets of data is known as Multivariate Data Analysis. It is an organised approach to analyse and interpret data for specific situations. Say for instance the ad agency has given you three different commercials on the same topic for promotions now it is time for a decision which of these three will actually attract target audience and by what ratio? A research and marketing analyst would face such questions almost every-day and will have to find a fitting analysis technique which could deliver the desired results and help make a decision that works. 

Meaning and Uses

Multivariate Data Analysis is a statistical technique used to analyse data that originates from more than one variable. These variables are nothing but prototypes of real time situations, products and services or decision making involving more than one variable. In this era of information, although we have data available in abundance and the technology needed to obtain a distinct depiction of current status is present, it is still a challenge to develop intelligent decisions.

Before we talk in detail about what multivariate data analysis is we must be familiar with a few things such as the term variate which is a particular combination of variables, a variate is a single perceived value of a random variable, measured to be one of many possible realizations. An example of a Multivariate Data as a single unit derived from multiple variables could be credit card applicants being a single unit, whereas Income, spend pattern and payment pattern being the variables.

Multivariate Analysis is mostly concerned with two areas descriptive and inference statistics. In descriptive area we often get best linear combination of variables that are mathematically docile, whereas an inference is an educated guess, specifically used to save analysts time from digging too deep into the data.

Multivariate Analysis can be used to read and process data stored in various database from the rows and columns of the database table to meaningful data. This technique is used to get an overview of a table in a database often called as factor analysis that reads heavy patterns in the data such as trends, groups, outliers and their repetitions forming a pattern. 

Techniques of Multivariate Data Analysis

There are many techniques of Multivariate Analysis starting with quality of the data to structural equation modelling, each one of the techniques has its own purpose, and are used depending on the data and the type of outcome realized by the data analyst. These techniques provide statistical data given a specific data set but requires caution when interpreting and putting them to use remember as I always say people do the most important part than what technology does for us.

The techniques are as follows:

  • Multiple Regression Analysis
  • Discriminant Analysis
  • Multivariate Analysis of Variance (MANOVA)
  • Factor Analysis
  • Cluster Analysis
  • Canonical Correlation
  • Classification Analysis
  • Principal Component Analysis

This article does nothing but scratch the surface of data science gives us a fare idea of how deep and important data analysis could be, after all it’s called a data science for a reason. Stay tuned will come back with other such new techniques of data analysis and visualization that can help out big time with your organisational needs.

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