**8 c] Explain different types of plots in relation plots.**

##### Relation Plots

Relation plots are perfectly suited to showing relationships among variables. A scatter plot visualizes the correlation between two variables for one or multiple groups. Bubble plots can be used to show relationships between three variables. The additional third variable is represented by the dot size. Heatmaps are great for revealing patterns or correlations between two qualitative variables. A correlogram is a perfect visualization for showing the correlation among multiple variables.

###### Scatter Plot

Scatter plots show data points for two numerical variables, displaying a variable on both axes.**Uses**

- You can detect whether a correlation (relationship) exists between two variables.
- They allow you to plot the relationship between multiple groups or categories using different colors.
- A bubble plot, which is a variation of the scatter plot, is an excellent tool for visualizing the correlation of a third variable.

###### Bubble Plot

A bubble plot extends a scatter plot by introducing a third numerical variable. The value of the variable is represented by the size of the dots. The area of the dots is proportional to the value. A legend is used to link the size of the dot to an actual numerical value.**Use**

Bubble plots help to show a correlation between three variables.

###### Correlogram

A correlogram is a combination of scatter plots and histograms. Histograms will be discussed in detail later in this chapter. A correlogram or correlation matrix visualizes the relationship between each pair of numerical variables using a scatter plot. The diagonals of the correlation matrix represent the distribution of each variable in the form of a histogram. You can also plot the relationship between multiple groups or

categories using different colors. A correlogram is a great chart for exploratory data analysis to get a feel for your data, especially the correlation between variable pairs.**Examples**

The following diagram shows a correlogram for the height, weight, and age of humans. The diagonal plots show a histogram for each variable. The off-diagonal elements show scatter plots between variable pairs:

###### Heatmap

A heatmap is a visualization where values contained in a matrix are represented as colors or color saturation. Heatmaps are great for visualizing multivariate data (data in which analysis is based on more than two variables per observation), where categorical variables are placed in the rows and columns and a numerical or categorical variable is represented as colors or color saturation.**Use**

The visualization of multivariate data can be done using heatmaps as they are great for finding patterns in your data.