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Scatter Plots are a powerful tool in Tableau for visualizing relationships between two continuous variables. They allow us to detect correlations, outliers, and patterns in large datasets, helping businesses make data-driven decisions.
In this blog, we’ll walk through the process of creating and interpreting scatter plots in Tableau, complete with step-by-step instructions and visual examples to help you gain insights from the data effortlessly.
What is a Scatter Plot?
A scatterplot (also called a scatter chart) uses Cartesian coordinates to display values for two variables. Each data point on the scatterplot represents an individual record in the dataset, and the position of the point is determined by its values on the x-axis (horizontal) and y-axis (vertical).
Use Cases of Scatter Plots:
Understanding relationships between two continuous variables (e.g., sales and profit).
Identifying clusters of data points.
Detecting outliers that deviate from trends.
How to Create a Scatter Plot in Tableau?
First, ensure that your dataset is ready to use in Tableau. For this example, we’ll use a sales dataset containing Sales and Profit as our two continuous variables.
Drag and Drop the Fields to create a Scatter plot
Go to the Data pane on the left.
Drag Sales to the Columns shelf (x-axis).
Drag Profit to the Rows shelf (y-axis).
Drag the Product Type field to the Color option under the Marks card to assign different colors to each product type in the scatter plot.
Drag the Product field to the Detail option under the Marks card to add more data points that represent the sub-categories in the scatter plot.
Drag a dimension, such as State, to the Filters shelf to filter and focus on specific subsets of the data.
Right-click on State in the Filters shelf and select Show Filter to enable the filter control for your chart.
Filters are now enabled for the scatterplot, allowing you to view data on a state-by-state basis.
One of Tableau’s powerful features for scatter plots is the ability to add trend lines, which reveal correlations between variables. To add trend lines to your scatter plot, navigate to the top menu and select Analysis → Trend Lines → Show Trend Lines.
Remember to update the title of your scatter plot, and feel free to experiment with different colors and tooltip options to further customize your visualization.
Understanding the Interpretation of Scatter Plots
After creating a scatter plot, it’s important to understand how to interpret it effectively. The scatter plot helps you analyze the relationship between two continuous variables (Sales and Profit, in our case), and can provide valuable insights into patterns and trends.
Key Insights to Look For:
1. Positive Correlation
A positive correlation exists when both variables increase together. In a scatter plot of Sales vs. Profit, this means as Sales increase, Profit also increases. Visually, a positive correlation is seen when most points trend from the bottom-left to the top-right of the plot.
2. Negative Correlation
A negative correlation occurs when one variable increases while the other decreases. In the context of Sales and Profit, it means that higher sales lead to lower profits, which might indicate inefficiencies, high costs, or aggressive discounts.
In a scatter plot, this is depicted by points trending from the top-left to the bottom-right.
3. No Correlation
If there is no correlation, the points on the scatter plot will appear randomly scattered without any discernible pattern. This suggests that changes in one variable (Sales) have no predictable impact on the other variable (Profit). If the points are scattered randomly with no clear pattern, this suggests no strong relationship between sales and profit.
4. Clusters of Data
Clusters represent groups of products or transactions with similar sales and profit levels. For example, you might find that high-sales, high-profit products form one cluster, while low-sales, low-profit products form another.
5. Outliers
Outliers are points that deviate significantly from the overall trend. A product with very high sales but low profit might indicate inefficiencies or high costs.
Adding Trend Lines for Better Analysis
Adding a trend line in Tableau can help confirm or reject the observed correlation between sales and profit. If the trend line has a steep upward slope, it indicates that sales and profit are strongly positively correlated. A flatter trend line may indicate a weaker relationship.
In conclusion, scatter plots in Tableau provide invaluable insights into the relationship between two continuous variables, enabling businesses to visualize and analyze correlations effectively. By uncovering patterns, identifying clusters, and detecting outliers, scatter plots empower organizations to make informed, data-driven decisions. The ability to add trend lines further enhances this analysis, revealing the strength and direction of relationships between variables.
Ultimately, harnessing the power of scatter plots allows businesses to optimize their strategies, enhance profitability, and drive growth in a competitive market landscape.