You’ll agree when we say understanding the fundamental relationship between key metrics in data is critical to the growth of a business.
Why?
The key to solving a problem is identifying its root cause. And this implies digging deeper to uncover the causal factors and their relationship to the problem you’re trying to alleviate.
This is where Scatter Plot comes in.
Scatter Plots are best suited to visualize data for causal relationship insights. Visualizing data using this insightful and easy-to-interpret chart should not stress you or even consume your valuable time, especially if you’re an ardent user of Google Sheets.
Yes, you read that right.
Google Sheets comes with pretty basic Scatter Plot templates, which require a ton of customizations to align with your data story.
It turns out you don’t have to do away with this freemium data visualization tool (Google Sheets). You can supercharge it with third-party apps (add-ons) to access highly intuitive and easy-to-interpret Scatter Plot examples.
This blog will walk you through the easy-to-follow steps to get started with this visualization. You’ll come across a ton of Scatter Plot examples to get you started with data storytelling quickly. In this blog you will learn:
You don’t want to miss the rest of the blog if your goal is to elevate your Google Sheets game to the A-level.
Before we cover the Scatter Plot examples we promised, let’s go through the definition part.
A Scatter Chart (also called a Scatter Plot, Scatter Graph) is a visualization design that uses Cartesian coordinates to display values in dots.
Besides, this chart distills key insights into the collection of points, along x and y-axes.
So when should you use this chart?
Use a Scatter Plot to compare two key variables in your data to determine their relationship. For instance, you can use this chart to track the relationship between click-through rate and conversion metrics in digital marketing.
In this scenario, you would want to know whether the growth of click-through rate (CTR) impacts conversions.
Essentially, you can use Scatter Plot examples to determine relationships or associations between key data points.
The actual analysis comes in when you discern the type of relationship existing between key metrics you’re tracking closely. You can use Scatter graph to uncover hidden “cause-and-effect” relationships between two key variables in your data.
Below are the scenarios you can apply Scatter Plot examples to get the maximum from your raw data.
This section is loaded with a ton of Scatter Plot examples to get you started with this visualization faster.
As we said earlier, freemium data visualization tools like Google Sheets come with pretty basic Scatter Plot examples. You need to rework these charts, which means additional time spent.
If you feel you’ve outgrown the basic charts offered by Google Sheets and you’re on the hunt for hidden insights: try ChartExpo.
ChartExpo is a data visualization library that produces charts that are incredibly easy to interpret. Besides, it comes loaded with amazing advanced charts you’ll never find freemium data visualization tools, such as Excel and Google Sheets. If you want to create Scatter plot in Excel you can refer to our guide How to Make a Scatter Plot in Excel otherwise keep reading to continue in Google Sheets.
You can access ChartExpo charts on both Google Sheets and Microsoft Excel. To install the tool of your choice and create stunning visualizations within few clicks in your preferred platform, please utilize the following calls-to-action (CTAs).
You don’t need to learn programming or coding to use ChartExpo.
In the next section, you’ll come across 5 Scatter Plot examples created using Scatter Plot Generator.
Imagine you have data about orders, sales, stock availability based on different products and categories as shown in the data table below.
Product | Category | Number of Orders | Sales | Available in Stock |
Socks | Garment | 300 | 2300 | 6 |
Jeans | Garment | 400 | 2400 | 9 |
Headphones | Electronics | 500 | 2500 | 10 |
USBs | Electronics | 600 | 3000 | 8 |
Paintings | Decoration | 700 | 12000 | 7 |
Mirrors | Decoration | 800 | 13000 | 9 |
Knee Bands | Fitness | 900 | 8000 | 12 |
Yoga Mats | Fitness | 400 | 4500 | 15 |
Jogging Shoes | Footwear | 850 | 30000 | 18 |
Sneakers | Footwear | 1000 | 35000 | 22 |
Open your Google Sheets application.
Insights
Number of orders of for categories Footwear and Decoration are above the average. Whereas “Electronics” and “Garments” are below the average.
In fitness category, Knee band got no. of orders above average. And Yoga mats remain below the average.
Sneakers are the best outlier in this data. Scatterplot has significantly highlighted this. Moreover, larger the size of the dot, more product is available in stock. Sneakers and Jogging shoes are more in stock. Socks has least availability in stock.
Imagine you want to investigate whether there’s a relationship between the number of students and the marks scored.
Let’s assume the data below is what you collected.
No of students | Marks Obtained | Percentage Of Students |
5 | 40 | 2.5 |
6 | 60 | 3 |
25 | 70 | 12.5 |
11 | 65 | 5.5 |
30 | 80 | 15 |
4 | 50 | 2 |
6 | 55 | 3 |
10 | 75 | 5 |
14 | 90 | 7 |
18 | 45 | 9 |
20 | 40 | 10 |
22 | 95 | 11 |
2 | 100 | 1 |
11 | 35 | 5.5 |
16 | 25 | 8 |
Insights
Imagine you want to determine the relationship between the outdoor temperature and the cricket chirps Besides, you have gathered enough data(hypothetical data) samples for visualization.
Let’s visualize the data below using Scatter Plot examples.
Temperature (Fahrenheit) | Number of Chirps (in 15 Seconds) | Total Cricket |
57 | 18 | 2 |
28 | 20 | 5 |
64 | 21 | 10 |
65 | 23 | 15 |
68 | 27 | 6 |
71 | 30 | 8 |
74 | 34 | 10 |
77 | 39 | 15 |
20 | 10 | 10 |
24 | 8 | 8 |
25 | 7 | 7 |
58 | 5 | 2 |
71 | 2 | 10 |
74 | 14 | 5 |
77 | 30 | 7 |
20 | 34 | 8 |
24 | 26 | 3 |
25 | 16 | 4 |
58 | 8 | 2 |
71 | 12 | 1 |
Insights
It’s incredibly easy to interpret the Scatter Plot example above. The x-axis represents the number of chirps. On the other hand, the y-axis represents the temperature. Size of the Dot represent no. of Chirps
Let’s suppose you’re a retail store owner and you want to assess the relationship between cost, number of orders, and profits. Let’s assume the table below represents your data.
Products Type | Products | Profit | cost | no. of orders |
Cosmetic | Face Primer | 15.79 | 90 | 10 |
Cosmetic | Foundation | 20.13 | 70 | 12 |
Cosmetic | Concealer | 38.62 | 190 | 9 |
Cosmetic | Blush | 34.62 | 880 | 16 |
Cosmetic | Highlighter | 71.84 | 900 | 22 |
Cosmetic | Bronzer | 71.84 | 600 | 23 |
Cosmetic | Powder | 32.77 | 600 | 42 |
Cosmetic | Eye Primer | 21.8 | 1300 | 19 |
Electronics | TVs | 110 | 590 | 28 |
Electronics | refrigerators | 12.61 | 390 | 11 |
Electronics | washing machines | 70.21 | 490 | 41 |
Electronics | air conditioners | 70.21 | 390 | 18 |
Electronics | printers | 68.83 | 260 | 17 |
Electronics | speakers | 17.55 | 210 | 2 |
Electronics | keyboards | 54.74 | 170 | 23 |
Electronics | e-readers | 12.66 | 170 | 13 |
Garments | mobile phones | 47.36 | 140 | 27 |
Garments | Sweater | 83.64 | 110 | 13 |
Garments | Hoodies | 83.64 | 110 | 12 |
Garments | T-shirts | 22 | 760 | 6 |
Garments | Jeans | 75 | 1500 | 7 |
Garments | sweatshirts | 11.75 | 1000 | 19 |
Garments | formal trousers | 98 | 150 | 10 |
Garments | polo shirts | 27.77 | 380 | 14 |
Insights
Imagine you want to know whether there’s a correlation between age and the performance of employees in different departments in an organization. Let’s assume the data below is what you collected, and you intend to analyze for insights.
Size of the dot in the below table showing the attendance (0.1 for min attendance and 1 for full attendance)
Department | Age | Performance score | Name | Attendance |
Research and development | 24 | 20 | Timothy | 0.5 |
Research and development | 26 | 30 | Richard | 0.5 |
Research and development | 29 | 25 | Michael | 0.5 |
Research and development | 23 | 27 | Paul | 0.4 |
Research and development | 30 | 28 | Bowles | 0.4 |
Research and development | 32 | 36 | Christopher | 0.6 |
Research and development | 45 | 34 | David | 0.5 |
Research and development | 27 | 42 | Joseph | 0.5 |
Research and development | 26 | 43 | Patrick | 0.4 |
Research and development | 40 | 47 | Pryor | 0.5 |
Accounts and Finance | 60 | 28 | Johnson | 0.4 |
Accounts and Finance | 55 | 60 | Colbert | 0.5 |
Accounts and Finance | 45 | 65 | Bowman | 0.4 |
Accounts and Finance | 50 | 50 | Francis | 0.4 |
Accounts and Finance | 42 | 55 | Collins | 0.5 |
Accounts and Finance | 30 | 58 | Jonathan | 0.7 |
Accounts and Finance | 60 | 56 | Eric | 0.6 |
Accounts and Finance | 29 | 53 | Pruden | 0.6 |
Accounts and Finance | 30 | 24 | Thompson | 0.6 |
Accounts and Finance | 40 | 59 | Frank | 0.6 |
Sales and marketing | 22 | 63 | Jerome | 0.7 |
Sales and marketing | 28 | 70 | Ronald | 0.6 |
Sales and marketing | 30 | 75 | Walker | 0.5 |
Sales and marketing | 44 | 86 | Guerrier | 0.6 |
Sales and marketing | 33 | 90 | Carlson | 0.7 |
Sales and marketing | 24 | 95 | Petersen | 0.6 |
Sales and marketing | 29 | 97 | Boyle | 0.6 |
Sales and marketing | 31 | 99 | Rendon | 0.6 |
Sales and marketing | 44 | 100 | Gomez | 0.6 |
Sales and marketing | 27 | 43 | Winship | 0.7 |
Scatter Charts come in different variants based on the following factors:
Scatter Charts are divided into 3 types, based on correlation:
In this Scatter Plot example, the data is plotted in dots, keeping the dependent and independent variables in the y and x-axes, respectively.
As shown (above), all the markers or data dots are closely arranged linearly so a trend line can be plotted. And, this means there’s a strong correlation between key data points.
Therefore, the diagram above qualifies to be called a Scatter Chart with a high degree of correlation.
In the diagram above, the data points are arranged somewhat closer to each other.
Essentially, there’re not fully linear, which means you cannot draw a straight line through them. However, as you observe closely, you’ll notice there’s a less significant relationship between the variables.
This chart is also known as a Scatter Graph with a low degree of correlation.
In this Scatter Plot example, data points are scattered all over the place. Besides, it’s not easy to decode relationships between data points.
This data visualization design qualifies to be called a Scatter Chart with no degree of correlation.
Keep reading because, in the coming section, we’ll take you through a ton of Scatter Plot examples to simplify everything.
Scatter Charts are widely used to display the relationships between two variables.
The relationships you can uncover using this visualization design are categorized as:
The dots, which appear on Scatter Plot examples, represent the individual values of each of the critical data points. More so, they allow you to extract trend insights from data faster.
You can use this insightful chart to uncover hidden correlational relationships that exist in your raw business data.
Interpreting Scatter Plot examples is incredibly easy.
The key to interpreting this chart is always to remember the independent variables (metrics) sit on the horizontal axis (x-axis). And, the dependent variables are situated on the vertical axis (y-axis) in a Cartesian plane.
Use a Scatter Plot to identify the general trend of your critical variables in your raw data.
Data points in this chart are grouped based on how close their values are, which makes it easier to identify outliers. You don’t want to base your business decisions on outliers because they are outright misleading.
Interestingly, the nature of the correlations can also be estimated based on a specified confidence level.
Each chart and graph you use to visualize raw data has its strengths and weaknesses. A Scatter Chart is not an exception. Let’s go through its advantages:
There are some weaknesses associated with a Scatter Plot Chart you have to keep in mind. Let’s check them out.
We recommend you dig deeper to find more insights to confirm what a Scatter Plot is displaying.
Remember, if the diagram shows no relationship; investigate whether the independent (x-axis) variable has been varied widely. Why?
Sometimes a relationship is not apparent because the data sample you’re using is smaller.
You can use both continuous and discrete data types with Scatter Plot charts. Why?
Continuous data makes it easier for you to measure trends and relationships between data. You can choose to use discrete data on one axis and continuous data on the other axis of a Scatter Chart.
For example: for the discrete data, you’d have to put it into some kind of quantified band, like let’s say 1-10 on a customer satisfaction score.
A Scatter Plot is a chart you can use to uncover hidden relationships between key variables, such as metrics you’re tracking in your data.
Data points in a Scatter Plot can have the following type of relationships, namely:
The Scatter Plot has two primary uses, namely, showing trends and relationships between key data points. Besides, dots in this chart can report the values of individual data points and the general trends and patterns that exist in data.
Use Scatter Plot examples to establish causal-effect relationships, especially when solving problems.
Google Sheets produces Scatter Charts that are very basic. Besides, the charts produced (by Google Sheets) require a lot of customization to align with your data stories.
The solution is to install third-party applications, such as ChartExpo, in your Google Sheets to access advanced and intuitive Scatter Plot templates.
Visualizing your data to extract trends and relationship insights should never stress you or even consume significant amounts of your valuable time.
Use a Scatter Plot because it’s designed primarily to display hidden relationships between variables. Besides, the dots or data point markers can easily show you the general trend of the variables.
Google Sheets has a Scatter Plot template that’s basic and less intuitive. The solution is not to do away with Google Sheets but take full advantage of add-ons, such as ChartExpo.
ChartExpo comes loaded with 50-plus advanced charts, including Scatter Plot examples. You don’t need coding or programming skills to visualize your data using ChartExpo’s expansive chart library. More so you can easily export your chart in PNG and JPEG, the world’s most recognized formats.
Sign up for ChartExpo’s 7-day FREE trial today to access intuitive and easy-to-interpret Scatter Plots for your data stories.
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