What enables you to read this blog?
Why could you read your school science books and understand Copernicus’s discovery stating that, indeed, the earth revolves around the sun and not vice versa?
How did you come to know that there also exists a concept like space debris?
You like to read, you learned science in school or you leveraged Google searches are some of the probable answers.
Now, go a few stages back and ask yourself that, as a school kid, didn’t you learn alphabets and progressively improved your vocabulary and language skills.
Seems nonsensical? But isn’t this the reality?
Artificial Intelligence (AI) is an advanced application of mathematical algorithms over data, and today’s emerging software platforms are rendering it a fast-changing character.
However, what remains the same is its base of which multivariate statistics is a crucial part.
Undeniably, multivariate statistical analysis forms the ABC of data science or artificial intelligence.
Like alphabets allowed you to understand the significance of Copernicus’s theory, multivariate statistical analysis (MSA) will allow you to rightly apply machine learning for problem-solving.
Understanding Multivariate Statistical Analysis (MSA)?
A research paper from ScienceDirect defines MSA as,
“Multivariate analysis is conceptualized by tradition as the statistical study of experiments in which multiple measurements are made on each experimental unit and for which the relationship among multivariate measurements and their structure are important to the experiment’s understanding.”
(Source: https://www.sciencedirect.com/topics/medicine-and-dentistry/multivariate-analysis – Olkin, A.R. Sampson, in International Encyclopedia of the Social & Behavioral Sciences, 2001)
This is a slightly intricate definition of the concept, right? Here is how Wikipedia defines MSA,
“Multivariate analysis (MVA) is based on the principles of multivariate statistics, which involves observation and analysis of more than one statistical outcome variable at a time.”
Finally, we have Anderson explaining the concept in plain terms in his book An Introduction to Multivariate Statistical Analysis.
“Multivariate statistical analysis is concerned with data that consist of sets of measurements on a number of individuals or objects. The sample data may be heights and weights of some individuals drawn randomly from a population of school children in a given city, or the statistical treatment may be made on a collection of measurements, such as lengths and widths of petals and lengths and widths of sepals of iris plants taken from two species, or one may study the scores on batteries of mental tests administered to a number of students.”
Altogether, what we see is that MSA or MVA involves analyzing multiple variables simultaneously.
And in today’s purely data-driven environment, you cannot escape MSA, since variables exhibit multiple complex interrelations.
Here are some real-life examples that show why MSA is inevitable for today’s AI evangelists:
- Your digital supply chains emanate extreme data volumes, and you cannot begin unearthing patterns for process reengineering without analyzing variable relationships
- Healthcare providers try to understand what factors impact patient satisfaction by analyzing multiple variables that combine into lateral components.
- You want to ensure absolute accuracy in forecasting demand and you leverage deep learning, and analyzing variables form the ground.
- You want to understand the sentiment towards your online stores, and Natural Language Processing (NLP)-led text analytics requires you to explore variable relationships.
Well, the list is large to be accommodated here.
Some links to give you a good technical introduction, before we move ahead to getting more familiar with the concept:
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