A scatter plot is a graph that displays a visualization of the relationship between two numerical variables. The variables are typically plotted on the horizontal axis and the vertical axis (X and Y axes), and the points on the graph represent the values of the two variables for individual data points. The closer the points are to each other, the stronger the relationship between the variables. Scatter plots are extremely useful and powerful since they allow users to immediately understand relationships between data and data trends, and they can be used to track dependent variables and independent variables.
There are all kinds of data sets that you could present in scatter charts, but here are a few of the most common data types you’re likely to see and some handy ways to use them.
Continuous data is data that is collected and analyzed at fixed time intervals. This type of data is often used to track trends or changes over time. It may also refer to data that’s tracked continuously, such as how master data management is used to track continuous data in real time to improve business processes.
You can improve existing products and services with continuous data by tracking how customers use your products and collecting customer feedback. A scatter diagram may be useful to show relationships between marketing for products and the actual sales. You might also use data visualization from scatter plots to determine which marketing campaigns are most effective and see relationships between your effective ones as well as determine patterns shared by less effective ones.
Categorical data, as the name suggests, represents data sets divided into different categories. This is useful for classification purposes, and it’s great for getting a better idea of your customer demographics in business. Categorical data is extremely valuable in data science filed as well, where it’s often used to train machine learning models. Organizing data sets of categorical data also helps you locate data anamolies that are difficult to classify through typical means.
Ordinal data is a type of data that is ordered, but doesn’t necessarily have a numerical value. For example, when you rank items from best to worst, that would be ordinal data. Another example would be if you asked someone to list their favorite colors in order, that would be ordinal data. You could also ask someone how happy they are on a scale of one to ten, and their answer would be ordinal data. While one through ten are numbers, there is no definitive numerical value for happiness, so the data must be ordered in a different way.
Using Data Types In Scatter Graphs
In order to create a scatter plot, the data must be in two columns in a spreadsheet. The first column is the x-axis value, and the second column is the y-axis value. There are several ways that data can be displayed on a scatter plot, such as displaying steady changes over time as a straight line. This is also called linear data, and the dots in scatter visualizations of this kind of data generally stay close to the trend line.
Quadratic data is data that follows a quadratic curve. This type of data can be used to model a variety of real-world situations, including situations where the relationship between two variables is not linear. Quadratic data can be graphed by plotting the points that represent the data on a coordinate plane, and then drawing a quadratic curve through the points.
Exponential data is data that is growing at an exponential rate. This can be due to a number of factors, such as population growth, the number of internet users, or the number of mobile devices. As this data continues to grow, it becomes increasingly difficult to track and manage. This data is often displayed as a curve with its height at the top of the y-axis that gradually moves toward the bottom of the x-axis as the scatter plot progresses.