Scatter plots are the graphs that present the relationship between two variables in a data-set. It represents data points on a two-dimensional plane or on a Cartesian system. The variable or attribute which is independent is plotted on the X-axis, while the dependent variable is plotted on the Y-axis. These plots are often called scatter graphs or scatter diagrams.
Scatter Plot Uses and Examples
Scatter plots instantly report a large volume of data. It is beneficial in the following situations –
- For a large set of data points given
- Each set comprises a pair of values
- The given data is in numeric form
The line drawn in a scatter plot, which is near to almost all the points in the plot is known as “line of best fit” or “trend line“. See the graph below for an example.
Types of correlation
The scatter plot explains the correlation between two attributes or variables. It represents how closely the two variables are connected. There can be three such situations to see the relation between the two variables –
- Positive Correlation
- Negative Correlation
- No Correlation
When the points in the graph are rising, moving from left to right, then the scatter plot shows a positive correlation. It means the values of one variable are increasing with respect to another. Now positive correlation can further be classified into three categories:
- Perfect Positive – Which represents a perfectly straight line
- High Positive – All points are nearby
- Low Positive – When all the points are scattered
When the points in the scatter graph fall while moving left to right, then it is called a negative correlation. It means the values of one variable are decreasing with respect to another. These are also of three types:
- Perfect Negative – Which form almost a straight line
- High Negative – When points are near to one another
- Low Negative – When points are in scattered form
When the points are scattered all over the graph and it is difficult to conclude whether the values are increasing or decreasing, then there is no correlation between the variables.