Q: What is the relative frequency of an interval and how is it calculated?

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If you have calculated a histogram of your data, the mode is the interval with the highest relative frequency. If you have not created a histogram, and your dataset contains finite numbers (fixed decimal numbers), with some numbers repeating, then those numbers that repeat the most would be the mode. Otherwise, if you do not group your data, where you select an interval to calculate relative frequency, then a mode is not identifiable.

It is the empirical or experimental probability.

Cumulative percentage is another way of expressing frequency distribution. It calculates the percentage of the cumulative frequency within each interval, much as relative frequency distribution calculates the percentage of frequency.

Are you talking about a histogram of the relative frequency distribution.

Probability of event = relative frequency = f/nf is the frequency of the event occurence in a sample of n observances.

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If you have calculated a histogram of your data, the mode is the interval with the highest relative frequency. If you have not created a histogram, and your dataset contains finite numbers (fixed decimal numbers), with some numbers repeating, then those numbers that repeat the most would be the mode. Otherwise, if you do not group your data, where you select an interval to calculate relative frequency, then a mode is not identifiable.

I think the answer is class and... go ask the teacher or flunk

Both divide the data into discrete groups or intervals. The frequency histogram gives the number of times the data occur in the particular group or interval, while the relative frequency histogram gives the fraction of times the data occur in the particular group or interval.

Frequency density refers to the number of data points within a certain interval or range in a dataset. It is calculated by dividing the frequency of data points in a particular interval by the width of that interval. This measure helps to visualize and compare the distribution of data in a histogram or frequency distribution chart.

It is the empirical or experimental probability.

basically this is an exampleAGE (YEARS) FREQUENCY FREQUENCY DENSITYFD= Frequency DensityAge : 0

Cumulative percentage is another way of expressing frequency distribution. It calculates the percentage of the cumulative frequency within each interval, much as relative frequency distribution calculates the percentage of frequency.

Relative frequency is the proportion of all given values in an interval, i.e., the frequency of the event/value divided by the total number of data points.In other words...If you picked 12 marbles out of a bag, and 9 of them were green, the frequency of green marbles would be 9... but the relative frequency would be that number (the frequency) divided by the total number of marbles... so the relative frequency would be 9/12 or 3/4.--Relative frequency is the time that you get something successfully over the total number of times attempted... for example.. you flipped a coin 10 times, and you got heads 4 times. the relative frequency would be 4 over 10.

absolute frequency is a term decribing the total number of trials you did. a relative frequency is the number of measurements in an interval of a frequency distribution. or the ratio of the number of times an event occurs in a series of trials of a chance experiment to the number of trials of the experiment performed. so the difference is one is the total trials, and the other...well it depends on which definition you picked...

absolute frequency is a term decribing the total number of trials you did. a relative frequency is the number of measurements in an interval of a frequency distribution. or the ratio of the number of times an event occurs in a series of trials of a chance experiment to the number of trials of the experiment performed. so the difference is one is the total trials, and the other...well it depends on which definition you picked...

Well, that's not much of a question. Perhaps you are asking: What is the frequency interpretation of probability? This is called the classical interpretation of probability. Given n independent and identical trials with m occurrences of of a particular outcome, then the probability of this outcome, is equal to the limit of m/n as n goes to infinity. If you are asking: How can probabilities be estimated given data, based on frequency approach? A table is constructed, with intervals, and the number of events in each interval is calculated. The number of events divided by the total number of data is the relative frequency and an estimate of probability for the particular interval.