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Scatter Plot Correlation Chart is among the best-suited visualization designs for displaying causal relationships in data.
The graph is amazingly easy to read and understand. Why?
Our brains can easily identify a trend using dots. For instance, dots progressing on an upward-right side symbolize a linear (causal-effect) relationship.
Learning how to create a Positive Scatter Plot is a huge leap toward crafting compelling data stories.
Narratives have an emotional appeal. Learning how to make easy-to-read and interpret charts, such as Scatter Plot, is an incredibly powerful addition to your data storytelling arsenal.
However, it can be time-consuming or overwhelming, especially if you’re an average Google Sheets user. The tool lacks a ready-made and visually appealing Positive Scatter Plot.
You can access ready-made and visually stunning Positive Scatter Charts by installing a particular add-on into Google Sheets.
A Scatter Plot shows the relationship between different variables. Data insights are displayed via a series of data points between an x- and y-axis.
Essentially, each of these data points looks “scattered” around the graph, giving this type of data visualization its name. The visualization has multiple monikers, such as Scatter Diagrams Or X-Y Graphs.
A Positive Scatter Plot shows significant associations or relationships between key variables in data.
Use the chart to compare two key variables in your data to determine the 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) has an impact on conversion value.
Essentially, you can use a Positive Scatter Plot to determine relationships or associations between key data points.
The actual analysis comes in when trying to discern the type of relationship that exists between key metrics you’re tracking closely.
The chart comes in handy in uncovering hidden “cause-and-effect” relationships between two key variables in data. In the coming section, we’ll address the following question: what are negative and positive associations in a Scatter Plot?
All correlations have two properties, namely strength and direction. The strength of a correlation is determined by corresponding data points.
On the other hand, positive and negative correlation determines the overall direction of the chart.
Both variables move in the same direction.
In other words, as one variable increases, the other variable also increases. As one variable decreases, the other variable also decreases.
Check out examples below.
In this chart variant, variables move in opposite directions.
As one variable increases, the other variable decreases. In other words, there is an inverse proportional relationship.
In the coming section, we’ll address the following question: when should you use a Positive Scatter Plot?
A positive correlation means that as the first variable increases, the second variable increases as well. This corresponds to points (and a line of best fit) that move up as you go from left to right.
A scatter diagram is primarily used to visually investigate the relationship between two variables, often an input and an output variable.
This is useful, especially in verifying changes in the input variable will have an effect on the output variable.
Check out more benefits below:
Use a Scatter Plot to identify the general trend of your key variables in your raw data.
Data points in this chart are grouped together 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 can be 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.
Negative correlation depicts a downtrend.
Key data points slope downwards from the upper-left corner of the chart towards the lower right. Data that’s neither positively nor negatively correlated is considered uncorrelated.
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 to always remember the following: independent variables (metrics) are found on the horizontal axis (x-axis). And, the dependent variables are situated on the vertical axis (y-axis) in a Cartesian plane.
A Scatter Plot is widely used by seasoned data visualization experts to display the causal relationships between two variables. The relationship between variables can be positive or negative, non-linear or linear, and/or, strong or weak.
So how can you read a Positive Scatter Plot?
In the coming section, we’ll cover how to create a Positive Scatter Plot?
Google Sheets comes with a basic Positive Scatter Plot. And this means you need to rework this chart, 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.
So, what is ChartExpo?
ChartExpo is an add-on you can install in Google Sheets to access advanced Charts, such as Scatter Plot.
ChartExpo produces charts that are incredibly easy to interpret. Besides, it comes loaded with many advanced ready-made and visually appealing Positive Scatter Plot Charts you’ll never find in Google Sheets.
You don’t need to learn programming or coding to use ChartExpo. Yes, it’s that easy to use this highly intuitive tool.
This section will use a Positive Scatter Plot to display insights into the table below.
Keep reading because you don’t want to miss this.
| Widgets Produced Per Day | % Widgets with Errors |
| 10 | 1 |
| 13 | 0 |
| 16 | 1 |
| 23 | 2 |
| 25 | 3 |
| 37 | 3 |
| 38 | 4 |
| 41 | 3 |
| 50 | 5 |
| 52 | 6 |
| 61 | 5 |
| 69 | 8 |
| 70 | 7 |
| 77 | 7 |
| 82 | 8 |
| 83 | 8 |
| 88 | 8 |
| 91 | 9 |
| 95 | 10 |
| 100 | 9 |
Install the ChartExpo add-on for Google Sheets from this link and then follow the simple and easy steps below.
All correlations have two properties, namely strength and direction. The strength of a correlation is determined by corresponding data points.
On the other hand, positive and negative correlation determines the overall direction of the chart.
Check for the correlation patterns to determine the direction of the line.
We often see patterns or relationships in scatter plots.
When variables in the y-axis increase, the corresponding variables in the x-axis respond by growing as well. A Positive Scatter Plot exists when there’s a directly proportional relationship between key data points.
The visualization design is amazingly easy to read and interpret.
Scatter Plot Correlation Chart is among the best-suited visualization designs for displaying causal relationships in data.
The graph is amazingly easy to read and understand. Why?
Our brains can easily identify a trend using dots. For instance, dots progressing on an upward-right side symbolize a linear (causal-effect) relationship.
Learning how to create a Positive Scatter Plot is a huge leap toward crafting compelling data stories.
Narratives have an emotional appeal. Learning how to make easy-to-read and interpret charts, such as Scatter Plot, is an incredibly powerful addition to your data storytelling arsenal.
However, you have to struggle to visualize your data using a Positive Scatter Plot, especially if you’re an average Google Sheets user. The tool lacks a ready-made and visually appealing Positive Scatter Plot.
Download and install third-party apps, such as ChartExpo, to access ready-to-go Positive Scatter Plot.
ChartExpo is an easy-to-use application you can easily download and install in your Google Sheets app. Besides, this tool comes loaded with insightful and ready-made charts. You don’t need programming or coding skills to visualize your data using ChartExpo.
Sign up for a 7-day free trial today to access an easy-to-interpret and visually appealing Positive Scatter Plot.
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