Relative Frequency Histogram

A relative frequency histogram displays proportions or percentages on the vertical axis, while a regular histogram shows actual frequencies or counts. Both share the same shape and use class boundaries or midpoints on the horizontal axis. Relative frequency histograms make it easier to compare data sets of different sizes by normalizing values to proportions of the whole.

The vertical axis shows relative frequencies as decimals or percentages, while the horizontal axis displays class boundaries or class midpoints. Each bar's height indicates the proportion of data falling within that class interval. For example, if a bar reaches 0.17 or 17 percent, that proportion of the total data set falls within that price range.

Relative frequency histograms simplify interpretation of large data sets by converting raw counts into proportions, making patterns and distributions easier to visualize and compare. This normalization approach reduces visual clutter and allows meaningful comparison across data sets with different total sample sizes.

Class boundaries are the precise limits separating each class interval, such as 10.5 to 16.5 dollars. Class midpoints are the center values of each interval. Either can label the horizontal axis, depending on the analysis purpose. Both approaches organize quantitative data into meaningful groups for visualization.

A percentage frequency histogram expresses data as percentages, while a relative frequency histogram uses proportions or decimals. Both visualize the same distribution shape and serve the same purpose of showing how often values occur relative to others. The choice between them depends on whether you prefer percentage or decimal representation.

The histogram's shape reveals how data is distributed across classes, showing whether values cluster in certain ranges or spread evenly. By examining bar heights and patterns, you can identify the most common price ranges, detect skewness, and understand the overall distribution characteristics of your quantitative data set.

Equal-width bars without gaps ensure that bar height directly represents relative frequency, allowing fair visual comparison across classes. This standardization prevents misinterpretation and makes the histogram a reliable graphical representation for checking how data is distributed across different price ranges and proportions.