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Q: A cumulative frequency distribution would provide?

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Not all statisticians would agree that the statement is true.

This will purely depend on the question, if you get a frequency chart, (containing only the frequency and how often this was brought, take, etc depending on the question,) add up the frequency one by one and you will have the cumulative frequency. You then (depending on the question) make a chart or a box-plot and follow the question (i.e what if the correlation shown? this would depended on the trend of the data.)

False. When the range is large you would use a grouped frequency distribution.

Hi im 15 n i am doing my maths coursework which requires me to make a few cumulative frequency curves. Basically all you do is add the frequency as you go along. for example if the frequencies were: 4 5 2 3 then the cumulative frequency would be 4 9 11 14 You would then use this by plotting it along the y axis. There is a little more but that's mainly what u need to know to get started.

It is called the mode.

When the event of interest is a cumulative event. For example, to find the probability of getting three Heads in 8 tosses of a fair coin you would use the regular binomial distribution. But to find the probability of up to 3 Heads you would use the cumulative distribution. This is because Prob("up to 3") = Prob(0 or 1 or 2 or 3) = Prob(0) + Prob(1) + Prob(2) + Prob(3) since these are mutually exclusive.

That would provide some evidence that the distribution is symmetric about the mean (or median).

That would provide some evidence that the distribution is symmetric about the mean (or median).

Cumulative Frequency - The purpose is to help understand the total frequency of everything UP TO a given value. By example: You could have a list of women heights and the frequency (or probability or fraction of the population) that you'll find women of each height. Or you could have a list of women heights and the frequency that you'll find women of that height OR SHORTER. This is "cumulative" in that it adds all the frequencies from zero up to that point. Often cumulative frequency is shown in a graphic rather than as a list of values as above. You might have the axis on the left (Y-axis) go from 0% to 100% and the horizontal axis on the bottom (X-axis) go from 0 cm to 300 cm. The line on the chart would show the percentage of women with heights at or under that X-value, and of course, it would be very close to 0% up to 100cm (assuming adult women), then increase to nearly 100% at 200cm, and be flat at 100% up to 300cm.

Cumulative frequency gives the total number of events that occurred up to some value. Perhaps I want to show the number of accidents that occur in a year by the drivers age. A cumulative frequency plot would show me the total number of accidents from young drivers, say under the age of 21. I could easily come up with statistics such as 80% of all accidents occur from drivers ages 16 to 55, by examining the cumulative frequency. Cumulative frequencies are used extensively in risk or reliability analysis. If I'm trying to find out how long light bulbs last, I may want statistics on the number that last less than 1,000 hours, or the lifetime as indicated by the manufacturer. Another example: I may want to test the brakes of a car. I want to know the chances that the car will skid a long distance (further than the car is supposed to), so the cumulative frequency (long distances without stopping) is important.

No. It would not be symmetric if the data classes were of different widths.

Frequency is how often something occurs. Frequency distribution is how often something occurs within a group of separate categories or ranges. Say you had a list of exam scores from a class of students. You might want to find out how many people got between 0% and 10%, how many got between 11% and 20%, how many got between 21% and 30%, and so on. All you are interested in is the how many as compared to who actually got the scores. What you would be doing is creating a frequency distribution table. You would be finding out how many people got results in the various ranges, or how the frequency of results are distributed across these ranges.

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