A scatter diagram is a type of plot used to represent the relationships between certain variables. Itâ€™s a useful type of diagram in that it can allow you to identify patterns in the data and see relationships that would not normally be that obvious. Unlike a regular plot, a scatter diagram makes it very clear when â€œcloudsâ€ of data points occur, allowing you to drive your analytical process to those areas of interest.
Scatter diagrams are flexible in their application, and they can easily be used in situations where both variables are independent of one another, or when one variable is directly tied to the other. There are some accepted standards with regards to how data should be represented in each of those two cases, but generally speaking, youâ€™re free to use your own convention if it suits you better. Thatâ€™s one of the features that make scatter diagrams so powerful and attractive as well â€“ their flexibility and ease of adaptation have been a prominent feature of the tool for a long time.
A great aspect of scatter diagrams is that they can allow you to easily identify outliers in the data â€“ that is, points which should normally not concern you. When you see that all of the results are primarily clustered in one area of the graph, with just one or two data points elsewhere, itâ€™s probably safe to assume that those distant points can be avoided as they are not directly relevant to your current analysis.
On the other hand, you can also use this kind of information to figure out what deviations your products normally demonstrate, allowing you to account for them in your future work. Knowing how those minor outliers tend to manifest themselves can be just as important as knowing that you can ignore them. It does take some time practicing the use of scatter diagrams before you will start to understand these points though, but this can be said for pretty much every tool used in statistical analysis and related fields.
Extending Beyond Two Dimensions
Another great thing about scatter diagrams is that they can let you easily plot data across multiple dimensions. 3D scatter diagrams are particularly popular, and they can show you a lot of information about a certain data set which would normally take a lot of effort to visualize properly through traditional means, including 2D scatter diagrams.
Of course, there is a limit to how far you can push this while still keeping it intuitive, and if you need to represent more complex relationships that span across several dimensions, you may want to look at an alternative mode of visualization. As great as scatter diagrams can be, they are not without their limitations in the end.
Something else that can come in handy when analyzing complex data sets is the fact that scatter diagrams can be easily linked to other types of data representation, such as matrices, allowing you to easily compute various points about that data. This can be extremely useful when combined with more in-depth, advanced analytical methods, and you can potentially even use something like machine learning to work with the data produced.
Scatter diagrams are great for use with digital analysis tools as well. If you frequently use digital analysis methods in your work, you will definitely appreciate what scatter diagrams can do for you in terms of simplifying your workflow and helping you store your data for the long run with ease.
Understanding how a scatter diagram represents its data and how itâ€™s different from other traditional modes of representation and plotting is a critical aspect of mastering analytical methodologies in general. You may not need to use scatter diagrams in your work anytime soon, but when the time comes for that, youâ€™ll definitely appreciate putting in the time to learn their more intricate details. Theyâ€™re great in terms of combining them with other analytical tools too, making them a fantastic addition to any leaderâ€™s set of tools.