Which data visualization technique is best suited for displaying frequency distributions in Power BI?

Which data visualization technique is best suited for displaying frequency distributions in Power BI?

• A. Pie chart
• B. Line chart
• C. Histogram
• D. Bubble chart

Answer: (C)

A histogram is the ideal data visualization technique for displaying frequency distributions because it effectively showcases the distribution of numerical data by grouping values into bins or intervals. Each bin is represented as a bar, with the height of the bar indicating the number of data points that fall within that range. This visual representation allows users to easily identify the shape of the distribution, including patterns such as skewness, modality (number of peaks), and the presence of outliers. The use of a histogram helps to reveal insights about the underlying structure of the data, such as central tendency and variability. It simplifies the understanding of how data is spread across different ranges, which is crucial for statistical analysis and decision-making. In contrast, other visualization techniques listed, such as pie charts, line charts, and bubble charts, focus on different aspects of data representation. Pie charts are more suitable for illustrating proportions of a whole, line charts are typically used for showing trends over time, and bubble charts are effective at representing relationships between three dimensions of data. While all these charts have their specific uses, they do not serve the primary purpose of displaying frequency distributions as effectively as a histogram does.

A histogram is the ideal data visualization technique for displaying frequency distributions because it effectively showcases the distribution of numerical data by grouping values into bins or intervals. Each bin is represented as a bar, with the height of the bar indicating the number of data points that fall within that range. This visual representation allows users to easily identify the shape of the distribution, including patterns such as skewness, modality (number of peaks), and the presence of outliers.

The use of a histogram helps to reveal insights about the underlying structure of the data, such as central tendency and variability. It simplifies the understanding of how data is spread across different ranges, which is crucial for statistical analysis and decision-making.

In contrast, other visualization techniques listed, such as pie charts, line charts, and bubble charts, focus on different aspects of data representation. Pie charts are more suitable for illustrating proportions of a whole, line charts are typically used for showing trends over time, and bubble charts are effective at representing relationships between three dimensions of data. While all these charts have their specific uses, they do not serve the primary purpose of displaying frequency distributions as effectively as a histogram does.