Data rules the world – it’s everything, and it’s one of the primary reasons it’s used to communicate findings in any endeavor. To present such data in an easy-to-digest format, you would need charts or graphs – and that’s where *scatter diagrams* come into play.

A scatter diagram is widely known as a correlation chart, scatter graph, or scatter plot. It’s one of the best tools used for determining the relationship between two variables. Ideally, one variable is plotted on the horizontal axis, while the other variable is plotted on the vertical axis.

The point of intersection pretty much shows the relationship pattern. Often, the scatter diagram is used to affirm or disprove the cause-and-effect relationship between two variables.

One of the primary objectives of a scatter diagram is to identify the relationship between two variables. Data set with over two variables will be somewhat difficult to study using a scatter diagram.

Still not sure of what a scatter plot is, and how to use it? Well, here is a better way of looking at it. Just keep reading you will find all answers and also will know about visualization library which helps to create this chart effortlessly.

- What is scatter diagram?
- Types of scatter diagram
- Why should you use scatter plots and scatter analysis?
- Kind of Data That can be Represented on a Scatter Plot
- How to interpret data analysis on scatter chart?
- How to Create a scatter diagram?
- Pros of using scatter plot chart
- Cons of using scatter plot chart
- Scatter diagram – when not to use it
- Scatter diagram considerations

**Definition: **A scatter plot (or x-y graph) is a chart designed for expressing the relationship between two variables or data points. Here’s how it works…

Two data points are plotted along the x and y axes. The x-axis represents the independent variable, while the y axis represents the dependent variable. The rule is not set in stone as you can find both axes representing independent variables in some scatter plots.

But why do experts use the term scatter plot? Well, when data is plotted on these two axes, the resultant plot would be scattered. As a marketer, you can use scatter plots to analyze keywords data in SEM marketing.

Now you have a good grasp of what a scatter plot is, and why it is named scatter plot, here is why you should use scatter plots and scatter analysis.

Well, you probably know the answer to the question by now. Beyond having a good grasp of what a scatter diagram is, you’ve got to know the various types of scatter diagrams.

The division of scatter diagrams is dependent on their correlation and slope type. When it comes to correlation, scatter diagrams are divided into three –

- Strong correlation\ Positive Correlation
- Moderate correlation\ Negative Correlation
- No correlation\ No Correlation

In this type of scatter plot, the data is marked as a dot, and the dependent variables are found on the y coordinate, while the independent variables are found on the x coordinate. By taking a close look at the graph, you would observe that the dots align in a linear pattern – and you can easily join them by drawing a straight line.

This arrangement denotes a strong relationship (or correlation) between the data. Experts choose to term it as scatter diagram with strong correlation (or high degree of correlation).

Also known as a scatter diagram with a low degree of correlation – the data points are somewhat non-linear, and it’s pretty difficult to draw a straight line through it. Furthermore, the data points (marked as dots) are arranged close to one another.

There is no degree of correlation or alignment in this type of scatter diagram. Most times, the data points are scattered all over the place, and it’s quite difficult to trace any form of relationship between them.

- Scatter analysis shows if there are any actual relationships between two variables or data sets. That is, it defines the relationship between two variables.
- For each independent variable, there are multiple values of dependent variables.
- There are pairs of numerical figures to help you pinpoint the value of each variable.

Generally, continuous data are represented on scatter analysis. Unlike discrete data where you get to have a pass/fail measurement, continuous data is used to measure an infinite set of data.

Some marketers prefer using continuous data on one axis of a scatter plot, while discrete data is used on the other axis. While using discrete data, you should quantify it by representing it with numbers (let’s say 1 – 10).

Scatter analysis is not all rosy – you would encounter some issues, and here are common issues you would likely encounter while using scatter plots.

There are trends in every market, and most times, statistical data are used in representing these trends – and that’s where scatterplots come into play. The problem lies in interpreting this data. Here is an easy way of interpreting scatter analysis.

A scatter plot is made of observations (or points), and each point has two coordinates. The first coordinate represents a piece of data – the data obtained by moving left or right, while the second coordinate represents the second piece of data. Typically, the second piece of data is found by moving up or down.

The first piece of data usually falls on the X coordinate, while the second piece of data usually falls on the Y coordinate. Ideally, a point (or dot) is placed at the intercession of these two coordinates, and the dot (or point) represents the observation.

Here’s something you need to know…

While interpreting a scatter plot, you can look for trends by moving from left to right. If there is an uphill pattern as you observe the trends from left to right, it implies a positive relationship between the two coordinates (X and Y coordinates). That is, as the value in the X-axis increases, the value in the Y-axis also increases.

If you observe a downhill pattern by moving from left to right, it implies a negative relationship between the X and Y coordinates. That is, as you move right, the X-value increases, while the Y-value decreases.

Finally, if there are no scatter diagram patterns (or relationships) between the coordinates, then there are zero relationships in the data observed.

Sometimes, you may observe a linear pattern in the data set, but it would not imply any type of dependency between the data set.

For instance, if a scatter plot of ad campaign spending is drawn, you would observe that marketers who spend much on ads tend to generate more results and make more profit. But that does not in any way indicate that shelling out many dollars in an ad campaign would yield profits.

It could be that marketers who spend more money on ad campaigns are more experienced, and as such, would likely write better ad copies and target the right audience.

You see, a linear relationship does not necessarily mean that one coordinate is entirely dependent on the other.

ChartExpo is one of the advanced visualization tools that can help you create a scatter diagram.

Department |
Name |
Age |
Performance score |
Punctuality |

Research and development | Timothy | 24 | 20 | 44 |

Research and development | Richard | 26 | 30 | 62 |

Research and development | Michael | 29 | 25 | 74 |

Research and development | Paul | 23 | 27 | 89 |

Research and development | Bowles | 38 | 32 | 96 |

Research and development | Christopher | 32 | 36 | 59 |

Research and development | David | 45 | 34 | 80 |

Research and development | Joseph | 22 | 42 | 50 |

Research and development | Patrick | 26 | 43 | 83 |

Research and development | Pryor | 40 | 47 | 82 |

Accounts and Finance | Johnson | 60 | 28 | 40 |

Accounts and Finance | Colbert | 55 | 60 | 75 |

Accounts and Finance | Bowman | 45 | 65 | 83 |

Accounts and Finance | Francis | 50 | 50 | 71 |

Accounts and Finance | Collins | 42 | 55 | 55 |

Accounts and Finance | Jonathan | 30 | 58 | 48 |

Accounts and Finance | Eric | 60 | 56 | 96 |

Accounts and Finance | Pruden | 29 | 82 | 75 |

Accounts and Finance | Thompson | 48 | 20 | 76 |

Accounts and Finance | Frank | 40 | 59 | 65 |

Sales and marketing | Jerome | 22 | 63 | 70 |

Sales and marketing | Ronald | 28 | 70 | 56 |

Sales and marketing | Walker | 30 | 75 | 87 |

Sales and marketing | Guerrier | 44 | 86 | 62 |

Sales and marketing | Carlson | 33 | 90 | 76 |

Sales and marketing | Petersen | 24 | 95 | 75 |

Sales and marketing | Boyle | 29 | 97 | 50 |

Sales and marketing | Rendon | 36 | 99 | 61 |

Sales and marketing | Gomez | 44 | 100 | 88 |

Sales and marketing | Winship | 27 | 50 | 59 |

Let’s say you’ve already got a data table, here is what you need to do.

If you have not downloaded this library yet, you can directly install ChartExpo Add-on for Google Sheets.

Once it is installed, you would have to navigate to the ChartExpo section on your Google Sheet Add-on by clicking on ChartExpo.

Next, click on the *create chart*.

Next, choose the scatter plot from the various options provided.

Choose your desired metrics and dimension and click on the create chart button. Your chart would be displayed on your screen.

Finally, you’ve got to track the relationship between the various variables.

Quadrants are representing different age group and there performance in their respective departments. Average line parallel to y axis is representing the average of age and average line parallel x axis is representing average of age. The size of the representing the punctuality of each individual. If you see how you can add title of chart, change colors of legends, and also can add trend lines for each department. Now, let start with how you can add title.

You can add title by clicking on Edit chart.

Now, you have to click on highlighted pen and you can add title of chart as per your requirement.

Click on highlighted pen and then go to Box from there you can choose color any of your choice. Using same method you can change colors of any other legend.

Now, click on Save to save all changes.

You can show trend lines for department.

He can do by clicking on Chart Setting on the right top of the window.

Now, he will click on Trend Line. After that he will click Show button. From Polynomial Line, he can choose whichever degree line he wants. In this case, let’s suppose he wants 3rd degree trend line. After that he will click on Apply.

Different colors are representing different departments and the trend line is representing performance trend according to age of each department. E.g. Sales and marketing department trend is going upward to age 36 and slowly decline afterwards. These insights might be helpful for a company.

Ideally, a scatter plot is used to show the relationship between two variables. It does not just show the relationship between variables, it also shows the nature of such a relationship.

The relationship could be linear or nonlinear, weak or strong, and positive or negative. Typically, the data points on the scatter plot represent the values of each data point. It also helps you to identify the overall pattern on the scatter diagram.

The Scatter diagram shows correlational relationships. Ideally, dependent variables are found on the vertical coordinates, while independent variables are found on the horizontal coordinates. This way, you get to easily identify the possible values on the vertical axis, provided the values on the horizontal axis are known.

The Scatter diagram shows patterns – it’s one of the core benefits of using it. Often, the grouping is dependent on the closeness of the values. This way, you get to easily identify any outlier.

Moving on, here are some interpretations of scatter diagram patterns you would likely observe in a scatter diagram.

- If the marked dots (data points) slopes upwards, from the lower-left area of the plot to the upper-right area of the plot, then you’ve got a positive correlation – and that’s a rise.
- If the marked dots (data points) slopes downwards, from the upper-left area of the plot to the lower-right area of the plot, then you’ve got a negative correlation – and that’s a fall.
- Uncorrelated (or null) data is obtained when there is neither a positive or negative correlation between the data set.

Yes, everything sounds all fine and juicy, but a scatter diagram is not designed to be used every time. Here is when not to use a scatter diagram.

But what are the pros and cons of using a scatter plot? Well, here is what you need to know.

There are advantages to using each data visualization tool. For a scatter diagram, here are pros for using the tool.

- Scatter plots are one of the best tools for showcasing the correlation between large data sets.
- It helps to pinpoint the relationship between two data points (or variables).
- It’s a viable method for showing non-linear relationships in raw data.
- Zero technical skill is needed, and it’s pretty straightforward to plot and understand.
- You can readily identify the maximum and minimum points in a scatter diagram, and tracing the data range of flow is easy.
- Scatter diagram helps you to pinpoint the exact value in a data set.

All is not rosy while using the scatter plot chart – here are the cons of using a scatter diagram.

- Scatter diagram is not ideal for showing the relationship of over two variables.
- It’s only recommended for data with numeric value.
- Sometimes, the relationship in the scatter diagram may be influenced by a third variable. Therefore, assuming that one variable is dependent on the other may be false and inaccurate.
- The more the scatter diagram shows a straight line, the stronger the relationship between the two variables.
- Sometimes, when the independent variable (x-axis) is varied widely, there will be no relationship in the scatter diagram.

A Scatter diagram offers an easy way of visualizing data, but that’s not to say it’s ideal for everything. Here are cases where it’s not ideal to use a Scatter diagram.

When there is lots of data in your scatter diagram, you may end up clogging the entire graph area, and it could lead to overplotting.

Often the data points in the graph area become so dense, and ultimately form a large blob. It’s quite challenging to read up anything from such a scatter diagram.

As earlier stated, you can curb the issue of overplotting by merely using alternatives like a heatmap. A heatmap pretty much shows the densest area of your data set.

Furthermore, you could use various color codes to create translucent marked points – that’s a more reliable way of creating a heatmap-like effect for your analysis.

All in all, you should avoid using a scatter diagram when there are lots of data points. There is a high probability of forming a large blob when a scatter diagram is used.

There are situations where you can predict that some data set is not related. In such situations, you’ve got to move on – no need of using a scatter diagram.

Why? Because there is no correlation, and a scatter diagram will do you no good. For instance, there would be no correlation for data of the weight of people in an area and the number of chairs in their homes.

Since the number of chairs in a home is not dependent on the weight of the person who owns the home, using a scatter diagram for such analysis would do you no good.

There are times when you can track a data set with more than one dependent variable. Often, it may be somewhat difficult to track such data sets.

If you desire to track a data set with more than one dependent variable, then you’ve got to change the color of each dependent variable. This way, you get to easily monitor each data point on the scatter diagram.

Here are some things to note before creating a scatter diagram.

- Regardless of the observed relationship in the scatter plot, do not assume that one variable is dependent on the other. A third variable could be the influencing factor.
- When observing a scatter diagram, the more straight line the marked dots are, the stronger the relationship between the variables.
- If the data is stratified, then there is a high possibility of zero relationship between the variables.
- If a line is unclear, then you should check out if there is reasonable certainty of the existence of a relationship between the two variables. If there is no relationship (or correlation), then the pattern may have been a random occurrence.
- If you observe no relationship in the scatter diagram, check if the independent variable covers a wide range. There are times when zero relationship implies that the data range is not wide enough.

A scatter plot is used for analyzing the relationship (or correlation) between two variables. With a scatter plot chart, you get to easily identify the relationship between two variables. Ideally, a variable is plotted on the Y-axis, while the other is plotted on the X-axis.

The intersecting point shows the relationship between the variables. In a scatter plot chart, one variable is plotted on the vertical axis, while the other variable is plotted on the horizontal axis.

The primary purpose of a scatter plot is to show the relationship between two variables. The marked dots show both the value of the data point and the overall pattern found on the data. Often, a scatter plot is used in identifying correlational relationships.

You can easily create a scatter plot in Google Sheets using ChartExpo Add-on.

Whether you are a marketer or analyst, you would agree that it’s quite difficult to consume raw, unorganized data. But data visualization makes it pretty easy to convert data into something understandable and useful.

With a scatter diagram, you get to easily identify the patterns and trends in any data. This way, you get to make more informed decisions. Also, a scatter diagram helps you see the big picture without missing a thing.

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