Answer :
Relative frequency distributions are generally more useful than frequency distributions when comparing identical-sized data sets.
Absolute frequencies as well as relative frequencies, such as percentages or ratios, can be displayed in frequency distributions.
It is possible to display the frequency distribution of data in a table or graph. Frequency tables, histograms, and bar charts are a few popular ways to display frequency distributions.
Tables of frequencies: A frequency table is an easy way to show how frequently a certain value or feature occurs.
Assume, for instance, that we have gathered information on height from a sample of 50 kids.
Both histograms and bar charts use columns drawn on a graph to visually represent frequencies.
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Relative frequency distributions are generally more useful than frequency distributions when comparing data sets
Final answer:
Relative frequency distributions are more useful than frequency distributions when comparing different data sets, especially of different sizes. Relative frequency gives a proportion of data points that fall into each category, allowing a more meaningful comparison across different data sets.
Explanation:
Relative frequency distributions are generally more useful than frequency distributions when you want to compare the distribution of a variable between various data sets, especially when the data sets have different sizes. This is because relative frequency refers to the proportion, or percentage, of data points that fall into a certain category, making comparisons more clear and meaningful.
A frequency distribution simply provides the frequency that a value occurs in a data set, while a relative frequency distribution provides the ratio of the frequencies of a value to the total number of outcomes. This characteristic of relative frequencies makes them particularly useful when comparing distributions from different data sets or when checking if data follows a specific distribution.
For example, the use of a relative frequency histogram and associated polygons is often more insightful than a simple frequency histogram because it displays the proportion of total data that falls within each category, not just the raw number of data points. This can help one to understand not just how many data points fall into a category, but also how this category compares to others relative to the whole data set.
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