Which type of chart is most effective for showing the relationship between two quantitative variables in Power BI?

Which type of chart is most effective for showing the relationship between two quantitative variables in Power BI?

• A. Area chart
• B. Bubble chart
• C. Scatter plot
• D. Radar chart

Answer: (C)

The scatter plot is the most effective chart for showing the relationship between two quantitative variables because it visually represents individual data points in a Cartesian coordinate system, where each axis corresponds to one of the variables. This allows for a clear examination of how one variable affects or correlates with the other. When using a scatter plot, you can easily identify patterns, trends, clusters, and outliers in the dataset. The position of each point on the graph shows the value of both variables, making it straightforward to see correlations, if any, between them. If the points are dispersed widely, they might indicate a weak correlation, while closely grouped points may suggest a strong correlation. In contrast, other chart types listed have different purposes: bubble charts add a third dimension (size of the bubbles) but can complicate the visualization of the relationship between just two variables. Area charts are useful for showing volume over time but don't effectively highlight the interaction between two quantitative variables. Radar charts are primarily designed for multivariate data comparison and are not suitable for illustrating relationships between just two quantitative variables clearly. This specificity of the scatter plot makes it the most appropriate choice for this analytical task.

The scatter plot is the most effective chart for showing the relationship between two quantitative variables because it visually represents individual data points in a Cartesian coordinate system, where each axis corresponds to one of the variables. This allows for a clear examination of how one variable affects or correlates with the other.

When using a scatter plot, you can easily identify patterns, trends, clusters, and outliers in the dataset. The position of each point on the graph shows the value of both variables, making it straightforward to see correlations, if any, between them. If the points are dispersed widely, they might indicate a weak correlation, while closely grouped points may suggest a strong correlation.

In contrast, other chart types listed have different purposes: bubble charts add a third dimension (size of the bubbles) but can complicate the visualization of the relationship between just two variables. Area charts are useful for showing volume over time but don't effectively highlight the interaction between two quantitative variables. Radar charts are primarily designed for multivariate data comparison and are not suitable for illustrating relationships between just two quantitative variables clearly. This specificity of the scatter plot makes it the most appropriate choice for this analytical task.