Univariate, Bivariate and Multivariate data and its analysis

In this article,we will be discussing univariate, bivariate, and multivariate data and their analysis.

Univariate data:

Univariate data refers to a type of data in which each observation or data point corresponds to a single variable. In other words, it involves the measurement or observation of a single characteristic or attribute for each individual or item in the dataset. Analyzing univariate data is the simplest form of analysis in statistics.

Heights (in cm)	164	167.3	170	174.2	178	180	186

Suppose that the heights of seven students in a class is recorded (above table). There is only one variable, which is height, and it is not dealing with any cause or relationship.

Key points in Univariate analysis:

1. No Relationships: Univariate analysis focuses solely on describing and summarizing the distribution of the single variable. It does not explore relationships between variables or attempt to identify causes.
2. Descriptive Statistics: Descriptive statistics, such as measures of central tendency (mean, median, mode) and measures of dispersion (range, standard deviation), are commonly used in the analysis of univariate data.
3. Visualization: Histograms, box plots, and other graphical representations are often used to visually represent the distribution of the single variable.

Bivariate data 

Bivariate data involves two different variables, and the analysis of this type of data focuses on understanding the relationship or association between these two variables. Example of bivariate data can be temperature and ice cream sales in summer season.

Temperature	Ice Cream Sales
20	2000
25	2500
35	5000

Suppose the temperature and ice cream sales are the two variables of a bivariate data(table 2). Here, the relationship is visible from the table that temperature and sales are directly proportional to each other and thus related because as the temperature increases, the sales also increase.

Key points in Bivariate analysis:

1. Relationship Analysis: The primary goal of analyzing bivariate data is to understand the relationship between the two variables. This relationship could be positive (both variables increase together), negative (one variable increases while the other decreases), or show no clear pattern.
2. Scatterplots: A common visualization tool for bivariate data is a scatterplot, where each data point represents a pair of values for the two variables. Scatterplots help visualize patterns and trends in the data.
3. Correlation Coefficient: A quantitative measure called the correlation coefficient is often used to quantify the strength and direction of the linear relationship between two variables. The correlation coefficient ranges from -1 to 1.

Multivariate data 

Multivariate data refers to datasets where each observation or sample point consists of multiple variables or features. These variables can represent different aspects, characteristics, or measurements related to the observed phenomenon. When dealing with three or more variables, the data is specifically categorized as multivariate.

Example of this type of data is suppose an advertiser wants to compare the popularity of four advertisements on a website.

Advertisement	Gender	Click rate
Ad1	Male	80
Ad3	Female	55
Ad2	Female	123
Ad1	Male	66
Ad3	Male	35

The click rates could be measured for both men and women and relationships between variables can then be examined. It is similar to bivariate but contains more than one dependent variable.

Key points in Multivariate analysis:

1. Analysis Techniques:The ways to perform analysis on this data depends on the goals to be achieved. Some of the techniques are regression analysis, principal component analysis, path analysis, factor analysis and multivariate analysis of variance (MANOVA).
2. Goals of Analysis: The choice of analysis technique depends on the specific goals of the study. For example, researchers may be interested in predicting one variable based on others, identifying underlying factors that explain patterns, or comparing group means across multiple variables.
3. Interpretation: Multivariate analysis allows for a more nuanced interpretation of complex relationships within the data. It helps uncover patterns that may not be apparent when examining variables individually.

There are a lots of different tools, techniques and methods that can be used to conduct your analysis. You could use software libraries, visualization tools and statistic testing methods. However, this blog we will be compare Univariate, Bivariate and Multivariate analysis.

Difference between Univariate, Bivariate and Multivariate data

Univariate	Bivariate	Multivariate
It only summarize single variable at a time.	It only summarize two variables	It only summarize more than 2 variables.
It does not deal with causes and relationships.	It does deal with causes and relationships and analysis is done.	It does not deal with causes and relationships and analysis is done.
It does not contain any dependent variable.	It does contain only one dependent variable.	It is similar to bivariate but it contains more than 2 variables.
The main purpose is to describe.	The main purpose is to explain.	The main purpose is to study the relationship among them.
The example of a univariate can be height.	The example of bivariate can be temperature and ice sales in summer vacation.	Example, Suppose an advertiser wants to compare the popularity of four advertisements on a website. Then their click rates could be measured for both men and women and relationships between variable can be examined