Относительное распределение частот

Frequency counts how many data values fall into each class, while relative frequency expresses this as a proportion or fraction of the total sample. Relative frequency is calculated by dividing each class frequency by the total number of data points. This allows comparison across datasets of different sizes and shows how common each value is in the dataset.

To calculate relative frequency, divide the frequency of a specific class by the sum of all frequencies in the dataset. For example, if a class has 5 observations and the total sample size is 100, the relative frequency is 5 divided by 100, which equals 0.05 or 5%. This decimal or percentage represents the proportion of data falling within that class.

The sum of all relative frequencies must equal one because relative frequencies represent proportions of the entire dataset. Since every data point belongs to exactly one class, the proportions across all classes must account for 100% of the data. If the sum differs significantly from one, it indicates a calculation error in the relative frequency distribution.

Relative frequencies can be expressed as fractions, decimals, or percentages. For instance, a relative frequency might be written as 1/20, 0.05, or 5%. All three formats represent the same proportion and can be converted between each other. The choice of format depends on context and audience preference for data presentation.

The key difference lies in the vertical axis. A regular frequency distribution graph displays actual counts or frequencies on the y-axis, while a relative frequency distribution graph displays proportions or relative frequencies instead. Both use the same class intervals on the horizontal axis, but relative frequency graphs allow easier comparison of datasets with different sample sizes.

Relative frequency distributions are commonly displayed using frequency polygons and histograms. These visual formats make it easy to identify patterns and compare proportions across classes. The relative frequency values on the y-axis replace simple frequency counts, providing a standardized view of data distribution regardless of sample size.

Relative frequency is useful because it shows the proportion of data in each class, making it easier to understand how common values are in a dataset. It allows meaningful comparisons between datasets of different sizes and provides clearer insight into data distribution patterns. Relative frequencies help identify which classes contain the most significant portions of the data.