Difference between Univariate, Bivariate and Multivariate Analysis

The basic difference between univariate, bivariate, and multivariate analysis is explained in the table added below:

Univariate Analysis	Bivariate Analysis	Multivariate Analysis
Univariate analysis involves the analysis of a single variable. This helps to describe and summarize the characteristics and distribution of that variable.	Bivariate analysis involves the analysis of the relationship between two variables. This helps to examines how one variable behaves in relation to another.	Multivariate analysis involves the simultaneous analysis of three or more variables. This helps to examines the complex relationships between multiple variables.
Techniques used in univariate analysis include: Measures of Central Tendency (Mean, Median, Mode) Measures of Dispersion (Range, Variance, Standard Deviation) Frequency Distributions, etc.	Techniques used in bivariate analysis include Correlation Analysis Scatter Plots Cross-Tabulations (Contingency Tables) Bivariate Regression Analysis, etc.	Common techniques used in multivariate analysis include: Multiple Regression Analysis Factor Analysis Principal Component Analysis (PCA) Cluster Analysis, etc.
Univariate analysis is useful for understanding the basic properties of a variable and identifying any outliers or patterns within it.	Bivariate analysis helps in understanding the strength and direction of the relationship between two variables. It is particularly useful for identifying associations or dependencies between variables.	Multivariate analysis allows for the exploration of interactions and dependencies among multiple variables. It helps in understanding the combined effect of several variables on an outcome or in identifying latent structures within the data.

Univariate Analysis

Bivariate Analysis

Multivariate Analysis

Univariate analysis involves the analysis of a single variable. This helps to describe and summarize the characteristics and distribution of that variable.

Bivariate analysis involves the analysis of the relationship between two variables. This helps to examines how one variable behaves in relation to another.

Multivariate analysis involves the simultaneous analysis of three or more variables. This helps to examines the complex relationships between multiple variables.

Techniques used in univariate analysis include:

Measures of Central Tendency (Mean, Median, Mode)
Measures of Dispersion (Range, Variance, Standard Deviation)
Frequency Distributions, etc.

Techniques used in bivariate analysis include

Correlation Analysis
Scatter Plots
Cross-Tabulations (Contingency Tables)
Bivariate Regression Analysis, etc.

Common techniques used in multivariate analysis include:

Multiple Regression Analysis
Factor Analysis
Principal Component Analysis (PCA)
Cluster Analysis, etc.

Univariate analysis is useful for understanding the basic properties of a variable and identifying any outliers or patterns within it.

Bivariate analysis helps in understanding the strength and direction of the relationship between two variables. It is particularly useful for identifying associations or dependencies between variables.

Multivariate analysis allows for the exploration of interactions and dependencies among multiple variables. It helps in understanding the combined effect of several variables on an outcome or in identifying latent structures within the data.

Bivariate Analysis

Bivariate analysis examines the relationship between two variables. It is often denoted as X and Y. It helps uncover correlations and associations between different factors in data analysis.

Bivariate Analysis

In this article, we will understand the meaning of bivariate analysis and its definition, as well as the types of bivariate analysis and applications of bivariate analysis.

Difference between Univariate, Bivariate and Multivariate Analysis

Bivariate Analysis

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