Complete Scatter Plot Data Examples

Every business sifts through a lot of data. When it comes to marketing, there is a large volume of data product sales, campaign performance, click-through rates, and the list goes on! One of the biggest challenges marketers face is to make sense of the data and put this data into use. This is where data visualization methods, like scatter plot comes into play!

When you want to compare the two values, understand the distribution of data, or study the correlation between the value sets, a scatter plot comes in extremely handy. Here, we will talk about complete scatter plot data examples.

Scatter Plots

A scatter plot or scattergram is a type of chart that shows a relationship between two variables. It also tells you about the distribution trends in the data sets. Ideally, it is used when there are too many data points or raw data, and you want to see the correlation between the variables or similarities in the data set.

In simple words, a scatter plot is a set of data points that are plotted along the X-axis and Y-axis. Thus, it is also known as an XY diagram. The shape these sets of data points create, reveals the relationships in a large amount of data (positive, negative, or zero).

Examples of Scatter Plots

Suppose you want to show the correlation between the price of homes and square feet area. In this case, you will take square feet on the X-axis and the price of the property on the Y-axis. As the square feet size increases (x-axis variable goes from the smallest to the largest), you will likely see a positive correlation.

However, several other factors will contribute to it, for example, renovation, location, etc. Still, you will most likely find a correlation between a home’s square footage and cost.

Let’s take another example when scatter plots are used as powerful visualizations for cause-and-effect relationships or see how one variable affects the other. For example, a digital marketer wants to see how does an increase in content influences the engagement rate. With scatter plots, they can see if there is a correlation between the two.

What to look for in this scatter plot?

Type of Correlation

When you draw a scatter plot diagram between the frequency of content and engagement rate, you need to look for a correlation. A positive correlation means one variable increases with the other. This means, when you share engaging content consistently, the engagement rate increases.

Furthermore, the higher is the number of followers, the more will be the engagement rate. Thus, smart marketers will try to increase the number of followers to improve brand engagement.

On the flip side, there could be a negative correlation too. When you publish more posts per day, the engagement might decrease. In such a situation, you need to be strategic in how often you should post content.

A positive correlation goes in an upward direction, whereas a negative correlation. But, in a negative correlation, the slope goes downwards, i.e., from upper left to lower right.

Strength of the Correlation

Just like the direction of the slope in a scatter plot shows the type of correlation between the variables, the grouping of the data points tells about the strength of the correlation. If data points group together tightly, there is a strong correlation. If the clustering is not strong or the data points are scattered, there is a weak correlation.

Thus, scatter plots not only show the correlation between the data but also helps you know if there is a specific pattern. For example, if an increase in the number of posts per day doesn’t show any effect on the engagement rate, it means there is little to no correlation or weak correlation.

Lines of Best Fit

When you are using a scatter plot to view the relationship between the variables, you draw a trend line to the scatter plot and find the best fit for the data. It makes the correlation between the points stand out from the data points.

Outliers

Outliers are the points that are far from the rest of the data points in a scatter plot and skew the data. But these points show that not everything fits into a pattern and help you find out where data didn’t follow the expected pattern. If you find an outlier, it is necessary to determine what caused this and look for ways to correct this unexpected behavior.

A Few Things to Consider

If you want to create a scatter plot, below a few things to keep in mind that will be helpful later-

Mean Lines Help You Study Data More Accurately

Mean lines provide the viewers with extra information to interpret the data more accurately. These lines help them see how different variables compare against the mean in a chart. For example, if you are using mean lines to show keywords, you can find out which of the keywords get the highest impressions. This way, you can sort the highest performing keywords and neglect the low-performing ones.

Keep Note of the Over plotting Issues

If the scatter plot appears littered with a lot of data points, it is due to overplotting. One solution is to adjust the scale so that all the dots can be interpreted clearly. If it happens due to the size of the dots, adjust the size to let the viewers view the graph easily. If it doesn’t work, consider making the dots semi-transparent or use bright colors for highlighting the areas of interest.

Making Predictions

One significant benefit of the line of best fit is that you can predict the data behaviors by extending the trend line. For this, data points need to have a strong correlation, though.

Conclusion

A Scatter plot is a type of data visualization that serves one main purpose, i.e., study the correlation between the data points. It shows the direction and extent to which the two data points or variables are correlated. It is a perfect chart type for determining the cause-and-effect relationships. You can share these graphs with your team to show the behavior of data points, how they are performing, and what they indicate. This allows you to make important future predictions and improve your strategy. So, try using a scatter plot to study the correlation that gives the most valuable insights into the data.