The last time you heard of a scatter plot, you likely remembered sitting in high school math class. But it turns out your teacher was right! Scatter plots really are useful. These graphs use dots dispersed along a horizontal and vertical access to help illustrate relationships between two numeric variables in a dataset.
A scatter plot is helpful in many contexts, like marketing analysis, sales reviews, and even sales forecasting. When you know two variables are related but you want to find out how strong that relationship is, you can use a scatter plot.
When it comes to data analysis, you need a good set of strategies in your toolbox that you can readily go to when you want answers. Sometimes, you just want to explore data further, and when questions come up, knowing the right method to map data can help you find answers fast.
In this guide, we’ll explore exactly what scatter plots are, how they work, why they’re valuable in business, and when to use them.
What Are Scatter Plots?
A scatter plot is a type of graph that maps the relationship between two numerical variables. In other words, two things you can measure. The point of the scatter plot is to show how strong the relationship between those variables are using dots.
How to Interpret a Scatter Plot
The closer the dots are to one another, the stronger their relationship. Dots that are far away on a scatter plot signify a loose relationship. You can also learn more about this relationship by looking at the pattern of the graph; if the dots move upwards on the vertical axis and the horizontal axis, then there is a positive relationship. If the dots on the vertical axis go downwards, then the relationship is negative.
A perfect positive relationship on a scatter plot happens when the line goes up at every point. It’s normal to notice some dots fall lower on a positive correlation graph. Paying close attention to any that fall outside a positive or negative relationship can help you identify outliers in your data.
You can describe the data in a scatter plot in several ways: linear or nonlinear, positive or negative, and strong or weak.
In positive relationships on a scatter plot, one variable increases causes the other to increase. In a negative relationship, one variable increasing usually causes the other to decrease.
The effect of variables on one another can be strong or weak. For example, a strong positive relationship would mean a clear, positive correlation between data points. On the other hand, a weak positive relationship would indicate there is not a clear or consistent trend between the variables.
Linear vs. nonlinear scatter plots are also helpful. They help you figure out how much variables affect one another. In a linear relationship, one variable is directly proportional to changes in the other variable. Linear relationships on a scatter plot are consistent and demonstrate a persistent increase or decrease over time.
Nonlinear scatter plots show a sudden spike or drop in trends. For example, imagine you run an HVAC company and want to analyze product trends using a scatter plot. You might notice a nonlinear relationship between air conditioner breakdown repairs during the summer based on the temperature. As it gets hotter, more people call for service, but it suddenly drops off in the fall and winter when people switch to heating.
How Are Scatter Plots Used in Business?
Scatter plots have a wide range of business applications, such as:
- Analyzing customer behaviors
- Tracking product performance
- Identifying the correlation between advertising costs
- Forecasting trends
- Performing risk assessments
- Conducting market research
When and Why You Should Use Scatter Plots
Now that we know what scatter plots do, it’s a good time to delve into the “when” and “why” behind them. You should use scatter plots to explore correlations between variables — like products and sales, consumer trends, risks and outcomes, and financial metrics.
There are different types of scatter plots that can represent individual data points or entire data sets as dots.
You should use a scatter plot when you want to:
- Evaluate the relationship between two variables
- Explore the connection between trends among quantifiable variables
- Identify patterns in data based on how dots on a scatter plot group together
Common Issues to Be Aware Of
Scatter plots aren’t always perfect. Sometimes, data can overplot, meaning that the data points group together so much that you’re unable to define clear relationships between them. When this happens, you can reduce the number of variables you use, change the form of the dots so they are more transparent, or consider using another type of chart — like a heat map — to better understand how the data variables relate to each other.
Another risk of using scatter plots is mistaking correlation for causation. In other words, thinking that because two variables affect one another, one of them must cause the other to increase or decrease. This is not always true.
Two variables may share a relationship, but they could also be influenced by external factors. It’s important to keep this in mind when interpreting scatter plots, so you get the most accurate results.
Unveiling Relationships with Scatter Plots in DashboardFox
Now that you're equipped with the knowledge of scatter plots, take your data exploration to the next level with DashboardFox! This user-friendly BI platform allows you to effortlessly create scatter plots and other data visualizations, revealing hidden trends and relationships within your data.
The beauty of DashboardFox lies in its intuitive drag-and-drop interface. No coding or extensive IT expertise is required! Simply select your data points, choose the scatter plot option, and watch your insights come to life.
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