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Bivariate analysis lets you study the relationship that exists between two variables. This has a lot of use in real life. It helps to find out if there is an association between the variables and if yes, then what is the strength of the association?

The bivariate analysis helps to test the hypothesis of casualty and association. It helps predict the value of a dependent variable based on changes to an independent variable.

**What is bivariate analysis?****How do you conduct a bivariate analysis?**- Types Of Bivariant Analysis
**How many types of bivariate correlations are there?****Bivariate data examples**

Bivariate analysis means the analysis of bivariate data. This is a single statistical analysis that is used to find out the relationship that exists between two value sets. The variables that are involved are X and Y.

- Univariate analysis is when only one variable is analyzed.
- Bivariate data analysis is when exactly two variables are analyzed.
- Multivariate analysis is when more than two variables get analyzed.

The results obtained from the bivariate analysis are stored in a data table with two columns. Bivariate analysis should not be confused with two-sample data analysis where the x and y variables are not related directly.

Here is how the bivariate analysis is carried out.

- Scatter plots – This gives an idea of the patterns that can be formed using the two variables
- Regression Analysis – This uses many tools to determine how the data post could be related. The post may follow an exponential curve. The regression analysis gives the equation for a line or curve. It also helps to find the correlation coefficient.
- Correlation Coefficients –The coefficient lets you know if the data in question are related. When the correlation coefficient is zero, the variables are not related. If the correlation coefficient is a positive or a negative 1, then this means that the variables are perfectly correlated.

**Scatter plots**– Here, you can plot dots on the x-axis and y-axis that help represent the relationship between the two variables. These dots and the patterns they create show the exact specific path by which further experimental analysis can be carried out.**Regression analysis**– In this, a line or a curve is plotted with the aid of two variables. The curve here can be exponential, while the line can be linear. This method helps to find a correlation coefficient that can be used for regression analysis.**Correlation coefficients**– This method helps derive a relation between two variables in the regression analysis. If the value of these coefficients is zero, there is no relation between the two variables, and they are independent of each other. These coefficients must have a value to find the correlation between two variables.

The kind of bivariate analysis depends on the kind of attributes and variables used to analyze the data. The variables may be ordinal, categorical, or numeric. The independent variable is categorical, like a brand of pen. In this case, probit regression or logit regression is used. If the dependent and the independent variables are both ordinal, which means that they have a ranking or position, then the rank correlation coefficient is measured.

The ordered probit or the ordered logit is used if the dependent attribute is ordinal. It is possible that the dependent attribute could be internal or a ratio like the scale of temperature. This is where regression is measured. Here is how we mention the kinds of bivariate data correlation.

**Numerical and Numerical**

In this kind of variable, both the variables of the bivariate data, which includes the dependent and the independent variable, have a numerical value.

**Categorical and Categorical**

When both the variables in the bivariate data are in the static form, the data is interpreted, and statements and predictions are made about it. During the research, the analysis will help to determine the cause and impact to conclude that the given variable is categorical.**Numerical and Categorical**

This is when one of the variables is numerical, and the other is categorical. Bivariate analysis is a kind of statistical analysis in which two variables are observed against each other. One of the variables will be dependent, and the other is independent. The variables are denoted by X and Y. The changes are analyzed between the two variables to understand to what extent the change has occurred.

Bivariate analysis is the analysis of any concurrent relation between either two-variable or attributes. The study will explore the relationship that is there between the two variables as well as the depth of the relationship. It helps to determine if there are any discrepancies between the variable and the causes of the differences.

The bivariate analysis examples are used to study the relationship between two variables. Let us understand the example of studying the relationship between systolic blood pressure and age. Here you take a sample of people in a particular age group. Say you take a sample of 10 workers.

The first column will show the worker’s age, and the second will record their systolic blood pressure.

The table then needs to be displayed in a graphical format to make some conclusion from it. The bivariate data is usually displayed through a scatter plot. Here the plots are made on a grid paper y-axis against the x-axis, which helps to find out the relationship between the given data sets.

A Scatter plot helps to form a relationship between the variables and tries to explain the relationship between the two. Once you apply the age on the y-axis and the systolic blood pressure on the x-axis, you will notice possibly a linear relationship between them.

*How to understand the relationship?*

The graph will show that there is a strong relationship between age and blood pressure and that the relationship is positive. This is because the graph has a positive correlation. So the older one’s age, the higher the systolic blood pressure. The line of best fit also helps to understand the strength of the correlation. The correlation is strong if there is little space between the points.

The correlation coefficient, or R, is a numerical value that ranges between -1 to 1. This indicates the strength of the linear relationship between two variables. To describe linear regression, the coefficient is called Pearson’s correlation coefficient. When the correlation coefficient is close to 1, it highlights a strong positive correlation. When the correlation coefficient is close to -1 then this shows a strong negative correlation. When the correlation coefficient is equal to 0, this shows no relationship.

The above example lets you understand what bivariate analysis is. Analyzing two variables is a common study used in inferential statistics and calculations. Many scientific and business investigations work on understanding the relationship between two continuous variables. The bivariate analysis answers the main question: if there is a correlation between the two variables if the relationship is negative or positive and what degree or strength of the correlation.

If you are interested in making it big in the world of data and evolving as a Future Leader, you may consider our** Integrated Program in Business Analytics**, a 10-month online program, in collaboration with IIM Indore!

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