5 Scatter Plot Examples to Get You Started with Data Visualization

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:

• What is Scatter Plot?
• Uses of Scatter Chart
• Top 5 Scatter Plot Examples
• Different Types of Scatter Plot
• Pros and Cons of Using a Scatter Plot

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.

What is Scatter Plot?

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?

Uses of Scatter 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.

• There’re pairs of variables in data.
• Dependent variables have multiple values for each figure associated with the independent variable.
• There are signs of a relationship between two variables in data.

How to Create Scatter Plot?

Top 5 Scatter Plot Examples

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.

How to Install ChartExpo in Google Sheets?

1. You can download ChartExpo directly from the Google Sheets. To get started, click on “Extensions” in the top toolbar.
2. In the menu that appears, go to “Add-ons” click the option “Get add-ons”.
3. Search for ChartExpo in the bar and click the Charts, Graphs & Visualizations by ChartExpo when it appears in the results.
4. Click the “Install” button. You will have to accept some permissions and you may have to confirm your Google account.

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.

Example 1

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.

• Open the worksheet and click the “Extensions” menu.
• Once the “ChartExpo-Best Data Visualization Tool” drop-down menu shows, click the “Open” and wait until ChartExpo add-on is loaded.

• Click the “Create New Chart” button.

• You’ll see list of charts provided by this library.

• You can scroll down to find Scatter Plot and click on it.

• You can paste the data in the sheet and then select the sheet name from the ChartExpo window.

• You should map the columns to the respective metrics and dimensions. E.g. in this example select “Sales” column for X-axis metric. “Number of orders” for Y-axis metric. Third metric is optional but still if you have a column you should map it, let’s select “Available in Stock” column as this is available in the example data table. For first dimension select “Product” column and for “Dot Color”, select “Category Column”
• As soon as you will click on “Create chart” button, ChartExpo will produce below visualization for you.

• You can click on “Edit chart” button to make some changes in the chart.

• To show the legend on the chart you can click on pencil icon, it will open “Legend Properties” window. Make sure to enable the “Show” button and then click on “Apply” button as shown in above image.
• You will notice that X-axis is only showing sales figures without dollar sign. You can click on pencil icon on this axis. From “Axis Bottom Properties” window, under the “Label Text” properties you can put “$” sign in Prefix and click “Apply All”. As shown in the image below.

• You can also put the header of this chart. By clicking on pencil icon on top. In Chart Header Properties window, you can set text in “Line1”. Make sure to enable the show button. You can also change the font styles properties in this window. Once all set, click on “Apply” button

• Final visualization will appear as below. You can click on “Save” button to persist all customized properties settings which you have updated on this chart.

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.

Example 2

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

• You can follow the same steps which were provided above in the Example 1 and then can map the columns “marks obtained” and the “no. of students” to the x and y-axis respectively. Last column “Percentage of Students” will be mapped on Dot Size Metric.

• You can click on “Create chart” to see the final visualization as shown below:

Insights

• 7 students have above-average
• 8 students have below-average
• There’s no correlation between the number of students and marks. The dots are scatted all over in the Scatter Plot example above.
• The size of the dot represents what is the percentage of students remained, who obtained the particular marks. Larger the dot, the larger the percentage of students who got such marks. E.g 15% of the total students got 80 marks while only 1% got 100 marks.

Example 3

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

• You can follow the same steps which were provided in previous examples. You can map the columns as shown in below image.

• When you will click on “Create chart” button, ChartExpo will produce below visualization.

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

Example 4

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

• You can follow the same steps which were provided in previous examples, you can map the columns accordingly as shown below

• After clicking on “Create chart” button, following visualization will be created.

Insights

• All the products that are above the average profit line are the most profitable.
• All the products on the right side of the average cost line are bleeding the business of its profits.
• Size of Dot is showing no. of orders.
• There’s no correlation between profits and costs. The dots are all over the place with no identifiable trend.

Example 5

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

• You can follow the same steps as mentioned in previous examples. You can create mapping of columns as shown below.

• Once you click on “Create chart” button, final visualization will appear as shown below:

Different Types of Scatter Plot

Scatter Charts come in different variants based on the following factors:

• Correlation
• Slope type

Scatter Charts are divided into 3 types, based on correlation:

• Strong 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.

• Scatter Chart with Moderate 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.

• Scatter chart with No 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 Plot Real-World Application

• Displaying hidden relationship between two variables

Scatter Charts are widely used to display the relationships between two variables.

The relationships you can uncover using this visualization design are categorized as:

• Positive or negative
• Non-linear or linear
• Strong or weak

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.

• Identification of Correlational Relationships

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.

• Identification of Data Patterns

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.

• Positive correlation depicts an uptrend. Essentially, in a Scatter Plot with a positive correlation, data points slope upwards from the lower-left corner of the chart towards the upper-right.
• A negative correlation depicts a downtrend. Key data points slope downwards from the upper-left corner of the chart towards the lower-right.
• Data points that are neither positively nor negatively correlated are considered uncorrelated (null).

What are the Pros and Cons of Using a Scatter Plot?

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:

• Scatter charts are incredibly crucial, especially if your goal is to display correlational insights.
• You can use this chart to display the correlation between critical metrics in your data.
• Data visualization experts consider a Scatter Plot an apt visualization design to show the non-linear relationship in data.
• Scatter Plots are amazingly easy to read, even for non-technical audiences.
• The range of flow of data in Scatter Plot examples is readily visible. And this means you can point out the maximum and minimum points in your data at a snap of the fingers.
• Many people think that scatter plot and line graph are similar but when you compare scatter plot vs. line graph you can find out both charts are different in tasks.

There are some weaknesses associated with a Scatter Plot Chart you have to keep in mind. Let’s check them out.

• You cannot use this chart to display the correlation of more than two key variables.
• Only data you can numerically compare is best visualized using a Scatter Plot.
• Although Scatter chart uncovers hidden relationships in data, avoid assuming a particular variable is the causal agent of the problem you’re trying to solve.

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.

What Type of Data Can you Visualize using Scatter Plot Examples?

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.

FAQs:

What is a Scatter Plot?

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:

• Linear or non-linear
• Positive or negative
• No correlation

What is the main purpose of a scatter plot?

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.

How can you create Scatter Plot in Google Sheets?

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.

Wrap Up:

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.