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We count the number of data values that fall into each class, and then draw the heights of the bars.
These heights can be determined by two different ways that are interrelated: frequency or relative frequency. The count of how many data values fall into a certain class constitutes the frequency for this class.
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This is because the heights relative to each other are the same whether we are using frequencies or relative frequencies.
Relative frequency histograms are important because the heights can be interpreted as probabilities.
All that we need to do is add up all of the frequencies.
We then divide the count from each class by the sum of the frequencies.
Classes with greater frequencies have higher bars and classes with lesser frequencies have lower bars. Relative frequency is a measure of what proportion or percent of the data values fall into a particular class.
A straightforward calculation determines the relative frequency from the frequency.
These probability histograms provide a graphical display of a probability distribution.
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To see the difference between frequency and relative frequency we will consider the following example.