Multiple Variate Analysis

What Are The Benefits of The Multiple Variate Analysis Course?

Great Learning offers a beginner-friendly certificate course in Multiple Variate Analysis which offers the following benefits :

• The course is ideal for aspiring data analysts or data science professionals who want to make a good career in this domain. 

• The course has a comprehensive curriculum carefully designed by professionals in this field to help you guide through various concepts of multivariate analysis step-by-step before progressing to the advanced concepts.

• The course covers topics such as data analysis, techniques of MVA, advantages of MVA, demonstration in Python, and more. You can access the course material as soon as you get enrolled in the course.

• It is a self-paced course available free of cost to anyone who wants to learn the basics of MVA techniques. You can easily pursue this course along with your full-time job by spending around two to three hours a week.  

• After successful completion of the course, you will also receive a shareable certificate. This can be attached to your resume to be shared with your existing or new employer to give you added advantage.

• We also offer placement assistance to assist you in preparing for interviews n and enhance your career prospects. 

What are the prerequisites to enroll in this course?

Basic knowledge of Python basics and Python libraries for Data Science is recommended to enrol for this course but it is not mandatory. The course is ideal for beginners as well as professionals.

History of Multiple Variate Analysis 

In 1776, Joey-Lou Lagrange came up with the derivation of the first multivariate technique known as "The Multivariate Normal Distribution". Fast forward to 1827, Laplace adds multiple linear regression to the derivation. In 1928, Wishart presented a paper with multivariate analysis that contained multiple variables. Later in the 1930s, R.A.Fisher published theoretical work on MVA along with inferential statistics. Since then, MVA techniques have been used in fields of mathematics, psychology, education, biology, and more. 

Multivariate Analysis Techniques and Examples 

Broadly speaking, multivariate analysis techniques can be divided into two categories-

• Dependence techniques 

• Interdependence techniques 

Dependence Techniques 

Dependence techniques or methods are used when one or more variables share a dependent relationship with one another. These methods are used to understand the correlation between cause and effect. For example, let us take more than two variables that can be used to describe or predict the value of the third variable. The dependent variable ‘weight’ shares a relationship with two independent variables ‘height’ and ‘age.’

Dependence techniques are majorly used in machine learning. The data analyst uses the data to build predictive models. In this method, independent and dependent variables are defined to make accurate predictions. 

Interdependence Techniques 

These techniques are used to find out the underlying patterns and structural makeup within a data set. Since it does not contain any dependent variables, the data analyst is not looking for causal relationships between the variables. Instead, the variables are grouped in meaningful ways to understand the structure of a data set. 

Below are some examples of various MVA techniques used in data science -

• Multiple Linear Regression : This involves a dependence method that contains both dependent and independent variables. The relationship between both these types of variables is explored to predict future outcomes accurately. For example, if a data analyst has to predict crop growth, the variable ‘crop growth’ is the dependent variable here. Now the effect of independent variables or factors is studied, which includes rainfall, temperature, humidity, and so on. The data analyst will prepare a multiple linear regression model that explores the proportion to which all these various factors influence crop growth.  

•  Multivariate Analysis of Variance : This method is used to measure the correlation of independent variables with that of dependent variables. The independent variables here are categorical in nature, whereas dependent variables are metric. The metric variable usually has a numerical value, while the categorical variable denotes a category. 

• Cluster Analysis : Cluster analysis is an interdependence technique that is used to group similar variables or data sets into a cluster. While grouping variables, data analyst ensures that the variables within a cluster are similar to each other. Afterward, intercluster and intracluster distances are also measured. The overall analysis helps to find patterns in the data and understand how the data is distributed.

• For example, let us suppose that a marketing company wants to segment its customers for their targeted campaigns. Cluster analysis can be used here to segment different customers into clusters with similar attributes such as geographical locations or spending habits. 

• Factor analysis Another interdependence technique is used in a data set with too many variables. In this method, the data analyst will prepare predictive models to find patterns in the data sets. While creating a model with many data sets, there is always a chance of overfitting. Overfitting is an error that reduces the chances of predicting an accurate outcome. By detecting data sets that are too closely related to each other, the data analyst can undergo factor analysis to analyze data sets and make subsequent predictions. 

• For example, if there is a data set containing an individual’s income, education, occupation, etc., then these variables have a high degree of correlation between them. Therefore, these can be grouped into one variable - socioeconomic status. By reducing the variables into one group, we are not losing any crucial information. These variables adequately indicate attributes of individual variables. Once the data is streamlined, the data analyst can further analyze it to make predictions. 

• Multiple logistic regression : This technique is used to predict a binary outcome which means that there can be only two outcomes. Using interdependent variables, the data analyst can find out whether an event will occur (1) or not (0). 

For example, an insurance firm wants to know whether each potential customer will make a claim or not. Using logistic regression analysis, the data analyst will include multiple interdependent variables such as age, income, occupation, health condition, and so on. These variables are then used to build a model to calculate the probability of the event or making a claim.