# Bivariate analysis

The study of a relationship between two variables is called bivariate analysis. Out of those, one variable is a dependent variable, and the other one is an independent variable. A dependent variable depends on the independent variable whose value changes in specific relation to the independent variable. The independent variable depends upon the practical values and not on any other variable.

The term bivariate analysis is incorrect to mention the samples having different data analyses where two variables are not connected.

## How to plot a bivariate analysis?

1. Scatter plots – One can plot dots on the x and y axes to represent the relation between two variables. These dots show specific patterns by which further experimental analysis can be carried out.
2. Regression analysis – In this, a curve or line is plotted with the help of two variables. The curve can be exponential, and the line can be linear. It helps to find a correlation coefficient that is useful for regression analysis.
3. Correlation coefficients – It is helpful to 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 both are independent of each other. There must be a value of these coefficients to find the correlation between two variables.

## Types of bivariate correlations

1. Numerical and numerical – In this bivariate correlation, both the variables have a numerical value.
2. Categorical and categorical – In this, both the variables are in static form. Some statements and predictions are made in this type of correlation. This deals in the cause and impact type of analysis.
3. Numerical and categorical – In this, one variable is numerical, and the other is categorical.

## Example of bivariate analysis

Consider cropland in a small village area. The land gives different crop yields in different seasons. Therefore, the farmer, who owns the land, gets different returns in different seasons. When we make a correlation between the crop yield and the weather, we will get a pattern. This pattern can help the farmer know which season is giving how much crop yield. With this data, he can sow more or less to get maximum profit depending upon the season.
In this case, the two variables are crop yield and season, which makes a bivariate correlation between doing further analysis.

## How to plot bivariate relations?

1. Collect the data of the experiment for which we need to plot the bivariate graph.
2. Make two columns denoting the relation between them.
3. Plot two variables on the graph. One can use a scatter plot graph, a box plot, or a mosaic plot to make a graph for bivariate analysis. Take an independent variable on the x-axis and dependent variable on the y-axis for easy plotting.
4. Place the dots as per the data.
5. The bivariate graph is ready. It can be used to study how a specific pattern is formed for the experiment conducted.
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