How do you find the cumulative frequency distribution of a set of data? When you have a set of data that tells you different amounts of things that have different measures of something, you can find a cumulative frequency distribution. For example, a set of data might tell us that in some study of 100 people, 23 people eat 4 times a day, 49 people eat 3 times a day, 21 people eat 2 times a day, and 7 people eat once per day. Then, for example, the cumulative frequency of 3 is the frequency with which people eat 3 times a day OR LESS. Thus, we add 49 to 21 to 7, and get a cumulative frequency of 77. The cumulative frequency of a measure is the number of things who fit that measure or less. In this case, the measure is times eating per day, and the things are people. The cumulative frequency distribution is given entirely by the cumulative frequency associated with each measure. All of these values, together, describe the distribution.
In this lesson, we go over a discrete and a continuous case of cumulative frequency distributions. The above example is a discrete case, because you cannot eat 1.329859 times per day, for example. You can eat once, twice, three times, and so on. The number of times you eat per day is a discrete measure. A continuous measure is one like time, which can take on the value of any real number. A big distinction between discrete and continuous is that between any two distinct discrete measurements is a finite number of other possible discrete measurements. Between any two distinct continuous measurements is an infinite number of other continuous measurements. For example, if we are measuring how many children someone has, between 1 and 3, the only possible value is 2. If we are measuring how long someone took to run a marathon, between the values of 2 and 3 hours are 2.12345 hours, 2.123456 hours, 2.23451 hours, and so on, an infinite number of possible values.
I hope you find this video helpful, and be sure to ask any questions down in the comments!
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