Data analytics is all about examining different variables to determine how they affect particular situations and outcomes. Use multivariate analysis when working with data that has more than two variables.
The term “multivariate analysis” refers to a broad range of statistical techniques, not simply one particular method.
These techniques enable you to understand your data more thoroughly in light of particular business or real-life circumstances. So, if you want to be a data analyst or data scientist, you should understand multivariate analysis.
We’ll give a thorough introduction to multivariate analysis and look into more detail about defining multivariate analysis and discuss several crucial methods you might employ while analyzing your data.in this article.
We’ll also provide a few real-life examples of multivariate analysis.
What is Multivariate Analysis?
Multivariate analysis is a process that involves multiple dependent variables and yields one outcome.
A statistical method for analyzing data including multiple types of measurements or observations is known as multivariate analysis (MVA). It might also entail resolving issues where many dependent variables are examined alongside other variables at the same time.
For instance, depending on the season, we are unable to predict the weather for any given year. There are numerous factors, including precipitation, humidity, and pollution. You will learn about multivariate analysis in this section, along with its origins and various applications.
There are three categories of analysis to be aware of:
- Univariate analysis: Univariate analysis focuses on examining a single variable
- Bivariate analysis: bivariate analysis involves the analysis of two variables together.
- Multivariate analysis: multivariate analysis encompasses all statistical techniques used to analyze more than two variables simultaneously. This approach aims to identify patterns, correlations, and relationships between several variables at once, providing a deeper and more comprehensive understanding of a given scenario compared to bivariate analysis.
The History of Multivariate analysis
R.A. Fischer, Hotelling, S.N. Roy, and B.L. Xu et al. did a lot of fundamental theoretical work on multivariate analysis in the 1930s. It was widely used at the time in the disciplines of biology, education, and psychology.
Multivariate analysis started to become important in geological, meteorological, medical, sociological, and scientific fields in the middle of the 1950s as a result of the introduction and development of computers.
As time went on, more application fields were explored along with the ongoing development of new theories and new methods that were put to by practice. Modern computers enable us to perform quite complicated statistical analyses by using the multivariate analysis methodology.
Example of Multivariate analysis
Consider that you have been given a task to predict the company’s sales. You can’t just state that ‘X’ is the issue affecting the sales.
We are aware that a number of factors or variables will affect sales. Only multivariate analysis can identify the variables that will significantly affect sales. And there will typically be more than one variable involved.
As we all know, sales are affected by a variety of variables, including the product category, production capacity, geographical location, marketing effort, brand presence in the market, competitor analysis, product cost, and more. This study can be applied to any area in most fields, with sales serving only as one example.
Many businesses, including the healthcare industry, frequently use multivariate analysis. A group of data scientists made a forecast for the latest COVID-19 event, estimating that by the end of July 2020, London would have more than 5 lakh COVID-19 patients.
This analysis was based on a number of different variables, including governmental decisions, citizen behavior, population, occupation, public transportation, healthcare services, and the general immunity of the community.
According to a data analysis study by Murtaza Haider of Ryerson University, multivariate analysis is also used to determine how much an apartment costs and what causes that cost to increase or decrease.
Transport infrastructure was one of the key factors, according to that study. According to the analysis team, this was one of the least considered variables at the beginning of the study, but people were considering buying a home in a place with better transportation. But after examination, a few final variables emerged that affected the outcome.
Exploratory data analysis includes multivariate analysis. We can visualize a richer understanding of multiple variables via MVA.
The ideal method to use for multivariate analysis relies on the type of data you have and the issue you are attempting to solve. There are more than 20 different methods available.
The benefits and drawbacks of multivariate analysis
Advantages of multivariate analysis
The main advantage of multivariate analysis is that it considers more than one factor of independent variables that influence the variability of dependent variables, resulting in a more accurate conclusion.
The conclusions are more realistic and applicable to real-life situations.
Disadvantages of multivariate analysis
The main drawback of MVA is that it necessitates quite complex computations in order to reach a satisfactory conclusion.
It takes a lot of time to gather and tabulate all of the observations for all of the different variables.
How to choose right multivariate analysis Technique
The right multivariate technique is chosen based on-
a) Are the variables classified as independent and dependent?
b) If so, how many variables are considered dependent in a single analysis?
c) How are the dependent and independent variables measured?
This classification depends on whether the relevant variables are dependent on one another or not.
Multivariate analysis techniques: Dependence vs. interdependence
There are different techniques for multivariate analysis, which can be categorized into two main groups:
- Dependence techniques
- Interdependence techniques.
These techniques focus on different types of relationships within the data. Let’s explore each category:
Dependence Techniques
Dependence techniques are used when some variables are dependent on others. These methods examine cause and effect relationships, aiming to explain, describe, or predict the value of a dependent variable based on one or more independent variables.
Example: Dependence Techniques
For example, in a predictive model, the weight of an individual may be predicted using independent variables such as height and age. Dependence techniques are commonly employed in machine learning for building predictive models, where the analyst specifies the independent and dependent variables to train the model for making predictions.
Interdependence Techniques
Interdependence techniques, on the other hand, focus on understanding the structural makeup and underlying patterns within a dataset. In this case, no variables are considered dependent on others, and the objective is not to establish causal relationships. Instead, interdependence techniques seek to find meaning in a set of variables or group them together in meaningful ways.
These methods help uncover patterns and similarities in the dataset, enabling researchers to identify latent factors or clusters that provide insights into the data’s underlying structure.
Now, let’s explore some commonly used multivariate analysis techniques that fall within these categories:
- Multiple Linear Regression
- Multiple Logistic Regression
- Multivariate Analysis of Variance (MANOVA)
- Factor Analysis
- Cluster Analysis
objectives of multivariate analysis
(1) Data reduction or structural simplification: These techniques help simplify data while preserving valuable information. It will be easier to interpret as a result.
(2) Hypothesis construction and testing: The validity of particular statistical hypotheses is examined using the characteristics of multivariate populations. This may be done to support prior convictions or to confirm assumptions.
(3) Sorting and grouping: When there are multiple variables, we create groups of “similar” objects or variables depending on the characteristics that were measured.
(4) Exploring the relationship between variables: It’s interesting to look at how different variables relate to one another. Are all the variables independent of one another, or do some of them depend on others?
(5) Prediction Relationships between variables: In order to anticipate the values of one or more variables based on observations on the other variables, relationships between the variables must be established.
