Learn Scatter Plot and Best Fitting Lines

A scatter plot is a set of data points that shows the correlation between the variables. As the name depicts, these plots take a set of confusing, scattered data and turn it into something that makes sense. The best fitted lines within a scatter plot aids in data analysis and are used to study the nature of the relationship between the variables.

If you are a data scientist or you have a variety of statistical analysis to perform, scatter plots and the line of best fit are of great use. They are more than just a data visualization tool. As they help you discover new patterns or trends in data, it won’t be wrong to call them discovery tools.

In this article, we’ll learn more about scatter plots and best fitting lines.

Scatter Plot: A Useful Visualization Method

As discussed above, a scatter plot uses a set of data points plotted using the Cartesian coordinates on the x-axis and y-axis to represent variables. These data points take shape, which tells a story about the correlation in a vast amount of data. By placing the variables in the axis, you can figure out if there is a correlation between them. If the correlation exists, is it positive or negative?

In a positive correlation, the values increase together. However, in a negative correlation, one value decreases when the other increases. Sometimes, you will find that most points line up well except for some dots that don’t fit with the other dots. These data points are called outliers. Here, you need to decide if this data point represents any real-life variation or it is just an outlier.

A null scatter plot shows that there is no correlation between the data points. If the dots in a scatter appear to be in a straight line, it is called a linear graph. Similarly, there are exponential, and U-shaped scatter plots too.

When To Use A Scatter Plot?

Well, it’s quite easy to see the usefulness of a scatter plot. If there is a bunch of data and you want to find out the correlation between the data points, a scatter plot comes in extremely handy! Just create a scatter plot, draw the line of best fit, and determine the correlation.

A scatter plot provides you with a unique advantage over the other types of visualization charts. With scatter graphs, you can demonstrate and study the trends, clusters, patterns, and correlation in a cloud of data points.

Whether you want to compare profit with the expenditure in your business or study the buyers’ behavior of different age groups, you can interpret the correlation in a multitude of ways. Once you see the correlation (positive, negative, or zero), you can make better decisions.

What Are Best Fitting Lines or Trend Lines?

A line of best fit or a trend line is generally a guess about where a liner equation (y=mx + ab) might fall in the data points plotted in the graph. This straight line may pass through the center of data points, all of the data points, or none of the points.

Why Is A Trend Line Drawn?

The line of best fit is drawn to visually display the relationship between the variables that difficult to interpret in the graph. Data analysts fit lines to data to study the trends or patterns and show what the data is pointing towards.

How To Draw The Best Fitting Lines?

The best fitting line is the one that mimics the trend in the data. You can use computer graphing software like Excel to draw the best fitting line for your data. Or, you can look for a versatile data visualization tool that allows you to draw a scatter plot graph and a trend line to estimate the trends.

However, you can also draw a best-fit line manually with the eye-ball method. Below are the steps to do so-

• Look at the data points on the scatter plot. Does it look like a straight line, or is it flat?

• Try to approximate the trend in the data. Do you see a line trending in a direction?

• You have an idea of the trend in the data. Now, create a line so that approximately half points in the plot are above and half are below the line.

• Now position the line in such a way that it is close to as many points as possible. This is the best line of fit.

Another technique is the least square method, in which a line is positioned so as to reduce the sum of the squared distances from the line to the data points.

What Are The Best Practices To Draw A Scatter Plot?

Now that you have a good understanding of scatter plots let’s discuss some tips to design a scatter plot and get the most out of it.

• When there are so many variables in the data, it becomes difficult for the audience to see which variables represent which axis. So, draw a trend line or the line of best fit to help draw the correlation between the variables.

• Don’t draw too many trend lines, as it will make the data difficult to interpret. Prefer comparing two trend lines at a time.

• Use different-sized or colored dots to encode the additional data variables.

• Visualize the data section-wise. Group the points into quadrants to make sense of the comparison.

• Highlight the unique points of interest with colors or annotations.

Conclusion

Scatter plots and best fitted lines are a useful visualization method to analyze the data and understand what the trends are saying. However, just because you are able to find the line of best fit not necessarily means that it makes sense. According to PPCexpo, there are situations that the relationship between the two variables is driven by a third variable. In that case, further analysis for other potential variables needs to be performed. To make the data analysis easier, you can choose a good data visualization tool that meets your requirements. Once you integrate this tool into your existing setup, you will be able to make a better sense of the confusing data.