Those who complain when the knowledge of algebra comes to their rescue, then they might have never used a Scatter Plot chart. Yes, you heard that right! To represent the real-world results, you need to create a scatter plot chart that requires the knowledge of algebra.

The only difference is that during the school days, you needed to solve problems that seemed silly, like the height versus the weight of a group of people, the comparison between the sales of coffee and tea.

There are so many real word plots that you need to represent in there when it comes to scatter plot charts. Let’s take a step back and study the scatter plot a bit more.

What is a Scatter Plot?

A scatter plot is a representation of a comparison between two variables. The data is represented for comparison as X and Y variables. When you look at this graph, it is most likely to look like dots scattered all around the chart, which are named X and Y variables. The overall objective of this chart is to find out whether there is a correlation between the two variables.

The patterns of the scatter plots have few different features. Here are they:

Linear or non-linear:

A linear correlation forms a straight line to the data point, whereas the non-linear correlation will consist of curves or another form within the data point.

Strong or weak:

A strong correlation will have the data points closely correlated to each other, as the name already suggests, while a weak correlation will have data points far apart from each other.

Positive or negative:

A positive correlation will point upside, whereas a negative correlation will point downward side.

If you do not want any of the said features in your graph, there is no correlation between the data in the chart.

Examples of Scatter Plot:

Each of the charts for data representation has its own rules for going to the best data visualization to showcase the information.

Here are some of the best examples that will provide you with the idea that when is the best time to use the scatter plot chart.

The scatter data is all about finding the relation between the two variables with the help of the data provided. Say, for example, that you are running a coffee shop and are curious to find the reason for the downfall in the sales. In such a situation scattered plot chart is of great help.

You can collect all the data and make a chart out of it. However, let me tell you that in most cases, the only reason for the downfall in the sale of hot beverages is the hot weather. Yes, you heard that right! This all-time favorite hot beverage of people around the world hits its low only during the hot weather, so do not forget to add the weather factor in the data as well.

Benefits of Scatter Plot Charts:

  • Scatter Plot charts are easy to draw.
  • They are easy to understand
  • The value of unnecessary items does not affect this method. Such points are always isolated in the chart.
  • It makes an easy correlation between the two variables, making you figure out whether you are going right or wrong.
  • You have just to take a close look at the dots to understand the correlation.
  • It is very transparent and makes easy correlation.

Tips for designing a Scatter Plot

Here are some tips that will help you when designing the new scatter plot to find out the correlation between two variables.

De-clutter by removing trend lines:

During the analytical process, you might fit a model to describe the relationship between the variables. It adds clutter, and if they are underlying trend isn’t obvious, then implanting a line might lead to contention or confusion.

Make the overlapping data points transparent:

If there are too many overlapping data points, it can be hard to see what is going on. One of the tricks might be to play with the opacity of the data markers so that the individual data is visible.

You do not need a zero baseline:

Just like the line chart, the scatter plots encode data by position along the axis. It means that your baseline doesn’t need to start at Zero. You should be mindful anytime you deviate from the zero baselines as it may lead to a lot of confusion.

Create sections and labels for clarity:

Categorizing data points can make scatter plots are easier to consume. Without implementing a clear construct to read the scatter plot, the graph should be highly detailed and explanatory. It requires the readers to repeat the analysis to uncover the relationship.

When to avoid Scatter Plot?

The Scatter Plot chart is relatable only in certain situations. Here is when you should avoid scatter Plot chart:

Avoid scatter Plot when your data is not relatable

There are certain variables whose data makes no sense, and there is no correlation, so it will be useless to visualize your information in such a situation.

For example- if you are trying to get a survey of classroom students by their height and weight and number of pets they have at home, it will make no sense.

Avoid a scatter plot when you have a large set of data.

If you have so much information that the scatter plot clogs your entire chat, it will result in nothing but just over plotting. There will be no clarity, so there is no need to create a chart that will further complicate the situation.

Conclusion:

So, this is all the important information you need about the scatter plot chart and how it can be of great help when trying to find a correlation between two variables. So, what are you waiting for? Try out the scatter plot chart today. Good Luck!

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