Simple examples tend to be test scores and election returns. A frequency distribution could be graphed like pie chart or a histogram.
For big data sets, the stepped graph of a histogram is usually approximated by the smooth curve of a distribution function (known as a density function when normalized to ensure that the area under the curve is 1). The famed bell curve or normal distribution is actually the graph of one such function. Frequency distributions are particularly helpful in summarizing huge data sets and assigning probabilities.
A frequency distribution is probably the most common graphical tools employed to explain a single population. It is a tabulation from the frequencies of every value (or range of values). You can find a wide variety of methods to show frequency distributions, such as relative frequency histograms, histograms, cumulative frequency distributions and density histograms. Histograms show the frequency of components that take place within a certain array of values, while cumulative distributions show the frequency of elements that take place below a certain value.
Here are some examples of frequency distribution:
Construct a frequency, relative frequency and density histogram of net heat flux data at 130° E, 20° N for January 1960 to March 1998.
Mean of frequency distribution: A frequency distribution is any set up of data that exhibits the frequency of occurrence of various values of the variable or the frequency of occurrence of values dropping within arbitrarily described ranges of the variable known as class intervals.
Rules of thumb for organizing a set of data into class intervals:
1. Pick a class interval of such a size that between 10 and 20 such intervals will cover the whole range of the observations.
2. Choose class intervals with a range of 3,5,10 or 20 points.
3. Choosing class interval at a value that is multiple of the size of that interval.
4. Set up the class intervals in order of magnitude of values they contain with the class of biggest values on top.